APPLICATION OF IMAGE ENHANCEMENT AND

SEGMENTATION TECHNIQUES ON VEIN PATTERN FOR

BETTER IDENTIFICATION

Thesis submitted in

Partial Fulfillment for the award of

Degree of Doctor of Philosophy

in Computer Science & Engineering

By

S.SHARAVANAN

FACULTY OF ENGINEERING AND TECHNOLOGY

VINAYAKA MISSIONS UNIVERSITY

(VINAYAKA MISSIONS RESEARCH FOUNDATION DEEMED UNIVERSITY)

SALEM, TAMILNADU, INDIA

JANUARY 2016

VINAYAKA MISSIONS UNIVERSITY

SALEM

DECLARATION

I, S. Sharavanan, declare that the thesis entitled

APPLICATION OF IMAGE ENHANCEMENT AND

SEGMENTATION TECHNIQUES ON VEIN PATTERN FOR

BETTER IDENTIFICATION submitted by me for the Degree of Doctor

of Philosophy is the record of work carried out by me during the period

from 2009 to 2016 under the guidance of Dr. A. Nagappan, and has not

formed the basisfor the award of any degree, diploma, associate-ship,

fellowship, or other titles in this University or any other University or

Institution of higher learning.

Place: Salem

Date: Signature of the Candidate

VINAYAKA MISSIONS UNIVERSITY

SALEM

CERTIFICATE BY THE GUIDE

I, Dr. A. Nagappan, certify that the thesis entitled

APPLICATION OF IMAGE ENHANCEMENT AND

SEGMENTATION TECHNIQUES ON VEIN PATTERN FOR

BETTER IDENTIFICATION submitted for the Degree of Doctor of

Philosophy by Mr.S.Sharavanan, is the record of research work carried out

by him during the period from 2009 to 2016 under my guidance and

supervision and that this work has not formed the basis for the award of

any degree, diploma, associate-ship, fellowship or other titles in

this University or any other University or Institution of higher learning.

Place: Salem

Date: Signature of the Supervisor

iii

ABSTRACT

Many techniques have been proposed by the researchers earlier for

the identification of palm vein for authentication purpose. A set of

methods for the identification and authentication of Palm Veins ( PVs)

which uses various components of the PV images have been proposed for

better accuracy of identification and extraction. Also these methods have

to reduce the identification time with large number of input palm images. It

uses various geometric, wavelet features for the extraction of features of

PV and for the classification of PV used support vector machines which

produces high accuracy in classification.

In recent times, biometrics such as PVs, finger prints, face and iris

recognition have been extensively used in many employments together with

entry admission management, human being authentication for computers,

online banking, ATM‘s and foreign Transaction managements. PV

identification uses the exclusive prototypes of PVs to recognize the persons

at a sky-scraping stage of accuracy. This thesis offers a novel algorithm for

PV identification.

iv

A multi variant volumetric measure to perform palm vein

recognition is proposed. The method normalizes the image by resizing the

image and applies wavelet transform to increase the signal levels. The

transformed image is used to generate number of integral image and for

each integral image a set of Junction points and their coordinates are

identified. The identified features are presented as PV matrix and using

them, the Junctional volume and special volume to compute the

trustworthy measure of the PV given are computed. This method produces

efficient results in the false acceptance rate by reducing it. Also it improves

the accuracy of palm vein identification and authentication. This method

reduces the overall time complexity which is higher in other approaches.

A multi-level dorsal-deep Vein Pattern (VP) based PV recognition

approach is proposed. The method removes the noise and performs

histogram equalization to enhance the image. The enhanced image is

applied with wavelet analysis and splits the higher order and lower order

VP. Generated two different images are split into sub sample images and

their junction points are identified. Identified junction point matrix is used

to compute the dorsal depth and deep vein depth measures to compute the

cumulative weight. Based on cumulative weight an average distance

v

measure is computed to identify the person based on some threshold

value. The proposed method has produced efficient results and reduces

the false ratio and time complexity.

The two proposed approaches for the development of PV

authentication technology have been tested with different number of

classes and samples and produced efficient results in all the factors of

quality of PV recognition and authentication. Experimental results shows

the comparison of PV authentication accuracy produced by different

methods and it shows the proposed methods have produced 96%

accuracy which is better than the other methods at different number of

classes and samples.

vi

ACKNOWLEDGEMENT

I thank God Almighty who has been showering his blessings on

me bestowed strength, knowledge and courage all these days.

I express my sincere gratitude to our Founder of

Vinayaka Missions University, Dr. A. Shanmugasundaram and

I am grateful to respectable Madam Founder Chairman

Mrs. Annapoorani Shanmugasundaram for constant support.

I convey my sincere gratitude to our Chancellor Dr. A. S.

Ganesan and Vice - Chairman Dato Sri‘ Dr. S. Sharavanan, for

permitting me to do this research at this great institution V.M.K.V

Engineering College.

I would like to convey my thanks and gratitude to my guide and

philosopher, Dr. A. Nagappan, Principal, V.M.K.V Engineering College,

Salem, for having guided me in every aspect to complete the research

and thesis. I learned a lot from him. His positive attitude energy and

ability always motivated me to perform. His advice which I always

remember is ―Learn from Experience and Improve Your Work‖.

vii

This advice is always helped me not only to this research but also in

other aspects of my life.

My special thanks to our Vice Presidents Mr. J. Sathish Kumar

and Mr. N. V. Chandrasekar, Vice Chancellor Dr. V. R. Rajendran,

Registrar Dr. Y. Abraham and Dean (Research) Dr. K. Rajendran, of

Vinayaka Missions University, Salem, and to my colleagues and well

wishers who have helped me in one way or other in doing this research.

I would like to thank for CIE Biometrics for providing PUT Vein

Database, without that analysis on vein pattern would not be possible.

Last but not the least, I thank my parents, my wife and children

who were supporting me day in and day out during the course of my

research.

(S.SHARAVANAN)

TABLE OF CONTENTS

CHAPTER

NO.

TITLE PAGE

NO.

ABSTRACT iii

LIST OF TABLES

xiii

LIST OF FIGURES

xiv

LIST OF ABBREVIATIONS

xvii

1

INTRODUCTION

1

1.1 Biometrics and Palm Prints

1

1.2 Need for Palm Print Technology

8

1.3 Biometrics Based Palm Print Verification Process

9

1.4 Operation Modes of Biometric System

11

1.5 Advantages of Palm Print Biometrics

12

1.6 Disadvantages of Palm print Biometrics

14

1.7 PV Patterns

14

1.8 PV Authentication Technology

15

1.9 Principles of Vascular Pattern Authentication

15

1.10 Applications of Biometric Systems

17

1.11 Authentication With PV Images

22

1.12 Details of Technology 23

1.13 PV Acquisition Methods

24

1.14 Organization of The Thesis

25

1.15 Objective

26

2

LITERATURE SURVEY

28

3

PALM VEIN RECOGNITION SYSTEM USING

LOCAL BINARY PATTERN AND GABOR

FILTER USING CLAHE BASED CONTRAST

ENHANCEMENT METHOD

54

3.1 Introduction

54

3.2 Previous Research

55

3.3 Block Diagram

57

3.4 Methodologies

57

3.5 Input, Selected Region and Results of

Combine

Features 58

3.6 Architecture of PV Recognition 75

3.7 Junction Point (JP) Detection 78

3.8 Cross Correlation on Join Point Extraction 78

3.9 Limiting The PV Region 79

3.10 Extending and Sub-Sampling The Contained

Region 80

3.11 Extracting The PV Code By Using the Local

Binary Pattern 81

3.12 Matching The Extracted Codes With Enrolled

ones 83

3.13 Extracting Wavelet Transformed Feature:

Global Features 84

3.14 SVM Classification 86

3.15 Experimental Results 87

3.16 Summary 88

4 MULTI-VARIANT VOLUMETRIC MEASURE

ON UPPER EXTREMITYVP BASED PV

RECOGNITION USING WAVELET

TRANSFORM 91

4.1 Introduction 91

4.2 Methods Explored 94

4.3 Overview of Multi-Variant Volumetric Approach 99

4.4 Normalization 100

4.5 Wavelet Transform on Input Image 101

4.6 Canny Edge Detection

102

4.7 Integral Image Generation

102

4.8 Feature Extraction

103

4.9 Junction Point Identification

103

4.10 Junctional Volume Computation

110

4.11 Algorithm of Junctional Volume Computation

110

4.12 Spacial Volume Computation

111

4.13 Trustworthy Measure Computation

113

4.14 Summary

114

5

MULTI LEVEL DORSAL-DEEP VP BASED

PV AUTHENTICATION USING WAVELET

TRANSFORM

115

5.1 Introduction

115

5.2 Overview of Dorsal-Deep VP Based Approach

120

5.3 Noise Removal

123

5.4 Histogram Equalization

124

5.5 Wavelet Analysis

125

5.6 Sub-Sampling Image Generation

127

5.7 Junction Point Computation 127

5.8 Dorsal Depth Measure

129

5.9 Deep Vein Depth Measure

131

5.10 PV Recognition

132

5.11 Summary

134

6

RESULTS AND DISCUSSION

135

6.1 Multi - Variant Volumetric Measure on Upper

Extremity VP Based PV Recognition Using

Wavelet Transform 136

6.2 Multi - Level Dorsal - Deep VP Based PV

authentication Using Wavelet Transform 152

6.3 Comparative Analysis 163

7 CONCLUSION AND FUTURE WORK 165

8 REFERENCES 169

9 LIST OF PUBLICATIONS 189

LIST OF TABLES

TABLE NO.

Table 2.1

TITLE

Analysis of Various Techniques

PAGE NO.

53

Table 3.1

The Accuracy Rate of PV Images

88

Table 4.1

Displays the Values of Junction Point Matrix

109

Table 6.1

Details of Data Set Being Used

135

Table 6.2

Comparison of Resilience, Rotation and Noise

152

xiii

xiv

LIST OF FIGURES

FIGURE NO.

Figure 1.1

TITLE

Block Diagram of Biometric Verification System

PAGE NO.

10

Figure 1.2

PV Patterns

16

Figure 1.3

ATM with PV Recognition System

18

Figure 1.4

ATM with Small PV Authentication System

19

Figure 1.5 PV across Control Unit 21

Figure 3.1

Block Diagram of Gabor Filter and Local Binary

Pattern

57

Figure 3.2

Examples of Input PV Region

58

Figure 3.3

Examples of Selected PV Region

59

Figure 3.4

VP

60

Figure 3.5

Curvelet Decomposition Pattern

61

Figure 3.6

Gabor Texture Representation Regions

62

Figure 3.7

LBP Texture Representations

63

Figure 3.8

LBP and Gabor Performance

65

Figure 3.9

Individual Samples of Recognition

67

Figure 3.10

Samples Vary with Recognition Percentage – Set 1

68

Figure 3.11

Samples Vary with Recognition Percentage – Set 2

69

Figure 3.12

Comparisons of PCA and Gabor

70

xv

Figure 3.13 Comparisons of PCA and LBP 71

Figure 3.14 Comparisons of PCA and Gabor, LBP 72

Figure 3.15

Comparisons of PCA and Gabor, LBP With

Version Number of Samples

73

Figure 3.16

Comparison of Other Methods with proposed

74

Figure 3.17 Architecture of PV Recognition 77

Figure 3.18 Examples of Localizing the PV Region with Masks 80

Figure 3.19 Stretched Images of Figure 81

Figure 3.20

The LBP Operator

82

Figure 3.21

Graphical Representation of Accuracy Performance

89

Figure 3.22

Graphical Representation of Processing Time

90

Figure 4.1

Palm VP of Hand

92

Figure 4.2

Abstract VP of Human Hand

93

Figure 4.3

Proposed System Architecture-I

100

Figure 4.4

Block Diagram of Normalization

101

Figure 5.1

Displays the Abstract VP

116

Figure 5.2

Proposed System Architecture-II

122

Figure 6.1

Snapshot of Input PV Image Selected

137

Figure 6.2

Snapshot of Boundary Marked

138

xvi

Figure 6.3 Rotated Snapshot of PV Image and the

Region Marked to be Extracted

139

Figure 6.4

Snapshot of Extracted Region of Interest

140

Figure 6.5 Snapshot of Noise Removed Image 141

Figure 6.6

Snapshot After Background Removal

142

Figure 6.7

Snapshot of Normalized PV Image

143

Figure 6.8

Snapshot of Skeleton Identified Image

144

Figure 6.9

Snapshot of Identified Junction Points in the Image

145

Figure 6.10

Snapshot of PV Image Matched

146

Figure 6.11

Snapshot of Step by Step Result of Proposed

147

Figure 6.12

Snapshot of Input Image Selected for PV

Recognition

153

Figure 6.13

Snapshot of Region Extracted

154

Figure 6.14

Snapshot of Histogram Equalized ROI Image

155

Figure 6.15

Snapshot of Background Subtraction

156

Figure 6.16

Snapshot of Normalized Image

157

Figure 6.17

Snapshot of Junction Point Identified Image

158

Figure 6.18

Snapshot of Identified PV Image

159

xvii

LIST OF ABBREVIATIONS

ANN Artificial Neural Network

ASIFT Affine-SIFT

ED Euclidean Distance

EER Equal Error Rate

HD Hamming Distance

JP Junction Point

LBP Local Binary Pattern

LDP Local Derivative Pattern

NBI Normalized Back scattered Intensity

NIR Near InfraRed

PCA Principal Component Analysis

PV Palm Vein

ROI Region of Interest

SIFT Scale Invariant Feature

SVM Support Vector Machine

SURF Speeded-Up Robust Features

VP Vein Pattern

1

CHAPTER 1

INTRODUCTION

1.1 Biometrics and Palm Prints

Today, in our daily life, we are often being asked for verification of

our identity. Normally, this is done through the use of passwords when

pursuing activities like domain accesses, single sign-on, application logon

etc. In the process, the role of personal identification and verification

becomes increasingly important in our society. With the onslaught of

improved forgery and identity methods of impersonation, correct

authentication in previous ways is not sufficient. Therefore, new ways of

efficiently proving the authenticity of an identity at a low cost are heavily

needed. Various ways of approach have been explored to provide a solution

and biometric-based identification is proved to be an accurate and efficient

answer to the problem. Biometrics has been an emerging area of research in

the recent years and is devoted to identification of individuals using physical

traits, such as few based on hand geometry, iris, face recognition, finger

prints, or voices. As unauthorized users are not able to display the same

unique physical properties to have a positive authentication, reliability will

be ensured. This is much better than the current methods of using

2

passwords, tokens or personal identification number (PINs). At the same

time it provides a cost effective convenient way of having nothing to carry

or remember.

Identity management becomes more sophisticated due to the

development of digital processing techniques. Whatever be the

authentication system there is a presence of digital verification process

exists and that may be using any of the human anatomical part like nose,

eyes, palms, etc. The palm print is one among them which is becoming

more popular now a days. There are many researches going on palm print

recognition for various requirements. The digital image of palm print shows

the internal structure of nerves in human palms which is unique for each

human and how it could be used to identify a person is the vital problem.

Computer-based personal identification, also can be said as biometrics

computing began in 1970s. At that time, ‗Identity‘, the first commercial

system was developed, which measured the shape of a hand and focused

particularly on finger length. In the meanwhile, finger print-based automatic

checking systems were widely used in enforcement of law. Retina based

systems and iris-based systems were introduced in the mid-1980s. Today's

speaker identification has its root in the technological achievements of the

3

1970s; while signature identification and facial recognition are relative

newcomers to the industry.

In the ubiquitous network society, Individuals can also easily access

their information anytime and anywhere can be obtained and people are

also faced with the risk that others can easily access the same

information anytime and anywhere. Due to this risk, personal identification

methodology, which can differentiate among registered users and

imposters, is of grave importance.

Currently, passwords, Personal Identification Numbers (4-digit PIN

numbers) or identification cards are used for personal identification.

However, there is every likelihood that cards can be stolen or forgotten,

guessing passwords and guessing numbers are possible. Biometric

authentication technology is used to solve these problems, which identifies

people based on their unique biological information and it deserves

attention. In biometric authentication, an account holder‘s behaviors or

body characteristics are registered in a database and then compared with

others who may try to access that account to see if the attempt is legitimate.

The term biometrics refers to a scientific discipline involving

automatic methods for recognizing (verifying or identifying) people based

4

on their physical and/or behavioral characteristics. Many biometric systems,

exploiting these methods to establish identity, have previously been

presented in the literature among them, methods which make use of

biometric characteristics such as finger prints, face, voice, iris, retina, hand

geometry, signature or palm prints are the most common. While a

considerable research effort is directed towards the development of fast,

robust, efficient and user-friendly biometric systems, some major problems

that are still need to be tackled before they can be deployed on a larger

scale. One of the major challenges, which is yet to be solved, includes

increasing the performance of biometric systems in terms of recognition.

Towards this goal, a recent trend has emerged such as the employment of

multi-modal biometric systems which establish identity either by

considering several biometric modalities (e.g., the face, the iris, palm

prints, voice etc.) or by combining the recognition results of several

algorithms performed on the same biometric sample. The valid solution

from such approaches for the problem of recognition performance,

unfortunately user-convenience gets decreased, as it requires a much greater

effort from the user to operate the system or it increases the time needed to

process a single user. Hence the remedy is worse. From this point of view,

other solutions capable of increasing the recognition rates and not

5

influencing the convenience of using the biometric systems should be found

out. One possibility of increasing the recognition performance is to closely

examine the feature normalization techniques, which form the criteria of

decreasing the error rates of biometric systems, but have so far been

largely omitted in most research papers on the subject of biometrics.

Generally, only a sentence or two is devoted to the employed normalization

technique, even though representation of feature normalization is crucial

step in the design of a biometric system. Always feature normalization

techniques have a great impact on the procedure of constructing user

templates (or models), i.e., mathematical representations of the feature

vectors extracted from several measurements of the biometric

characteristic (e.g., palm prints) acquired during the enrollment stage, and

consequently on how user-specific biometric characteristics are modeled.

They represent a faster and more efficient way of boosting the

recognition performance of biometric systems which does not significantly

increase the processing time of a user.

These days many applications of biometrics are being used or

considered worldwide. Most of the applications are still at the early stages

of testing process, and end users find it as an optional. Any circumstance

6

that allows an interaction between man and machine is capable of

incorporating biometrics. Such situations may fall into a specific range

of application areas such as computer desktops, laptops, wired & wireless

networks, online banking and immigration, enforcement of law,

telecommunication synchronous and asynchronous networks. Fraud is

an ever-increasing problem and security is becoming a must in many walks

of life. Though research on the issues of finger print identification and

speech recognition have drawn considerable attention over the last 25

years and recently issues on face recognition and iris-based verification

have been studied extensively there are still some limitations to the

existing applications. Some finger prints of few peoples get worn away

due to the hand-work and some are born with unclear finger prints. The

existing iris-based identification system has not been proved to be

adaptive to eastern people who have quite different iris patterns from

those of people from western. voice based identification systems and

Face identification systems are less accurate and easy to be imitated. Efforts

on improving the present personal identification methods are to continue

and meanwhile newmethods are under investigation.

7

The palm print uses the similar set of characteristics used for finger

print recognition. Characteristics like ridge flow, ridge characteristics and

ridge structure of the raised portion of the epidermis are adapted. The data

represented by these friction ridge impressions either originated from the

same source or could not be made by the same source.

Palm print is based on ridges, principal lines and wrinkles on the

surface of the palm. A palm print refers to an image acquired of the palm

region of the hand. It can be either an online image (i.e. taken by a

scanner, or CCD) or offline image where the image is taken with ink and

paper.

The palm itself consists of principal lines, wrinkles (secondary lines)

and epidermal ridges. It varies from a finger print in that it also contains

other information such as indents, texture and identification marks which

can be used when comparing one palm to another.

Palm prints can be used for scientific tests or techniques used in

connection with the detection of crime or commercial applications. Palm

prints are normally found at crime scenes as the result of the offender's

gloves slipping during the time of crime, and thus exposing part of the

unprotected hand.

8

1.2 Need for Palm Print Technology

Biometrics has been an emerging field of research in the recent years

and is devoted to identification of individuals using physical traits, such as

those based on iris or retinal scanniridgesng, face recognition, finger

prints, or voices. As unauthorized users are not able to display the same

unique physical properties to have a positive authentication, reliability will

be ensured. Palm print is preferred compared to other methods such as

finger print or iris because it is always identical and can be easily

captured using low resolution devices as well as contains additional

features such as principal lines. Iris input devices are expensive and the

method is intrusive as people might fear of adverse effects on their

eyes. Finger print identification requires high resolution capturing

devices and may not be suitable for all as some may be finger

deficient. Palm print is therefore suitable for everyone and it is also non-

intrusive as it does not require any personal information of the user. Palm

print images are captured by acquisition module and are fed into recognition

module for authentication.

Compared with face recognition palm prints are hardly affected by

age and accessories.

9

Compared with finger print identification images of palm print

contain more information and need only low resolution image capturing

devices which reduce the cost of the system.

Compared with iris recognition the palm print images can be

captured without intrusiveness as people might fear of adverse effects on

their eyes and cost effective. Hence it has become such an important and

rapidly developing biometrics technology over the last ten years. Limited

work has been submitted on palm print identification and validation, without

being affected by the importance of palm print features. The functions of

the system is done by projecting palm print images onto a feature space

that spans the significant variations among known images.

1.3 Biometric Based Palm Print Verification Process

Biometric system is basically a pattern recognition system which

identifies a person using psychological or emphasizing behavioral metrics.

The characteristics such as 3D hand geometry, finger print and palm print

are read into system using some scanners and sensors and return a result.

Any kind of biometric system (Figure 1.1) has four stages named

1. Data Acquisition

2. Preprocessing

10

3. Feature Extraction

4. Feature matching

Figure 1.1 Block Diagram of Biometric Verification System

Data acquisition

The first stage of biometric verification process where the input

signals or images are gathered using input devices such as scanners. The

quality of signal given as input or image plays a vital role because the

quality of result depends on the quality of input signals or images.

Preprocessing

At this stage the signal quality and image is improved using various

11

preprocessing stages like filtering, normalization, rotation, segmentation and

noise removal.

Filtering: This is the process of selection of pixels from set of pixels

and the selection of signal or pixel depends on the value of pixel or signal.

Noise removal: This is a procedure of avoiding incomplete signals

and pixels from further stage of processing.

Feature Extraction: The process of extracting stable properties of

intra- class difference and high intra-class difference. They are used to build

the template of the data base.

Feature matching: Is a matching procedure to compute matching

score with the featured template and master template.

1.4 Operation Modes of Biometric System

Any kind of biometric system has three operating modes:

Enrollment

This combines the first three stages of the biometric verification

systemnamely (data acquisition / data developing skills, preprocessing, and

feature extraction). Any user has to be enrolled before verification

into the system by data acquisition and the features have to be extracted

12

and then stored into the system.

Identification

This tells the matching process of the biometric system. It works to

find out the user with the biometric features obtained from the user and

match with other biometric templates available in the system. It initiates

identification process without knowing the identity of the user.

Verification

The verification process is done when an identification process goes

successfully and then there is searching the record to identify the person

about name, ID card and other attributes.

1.5 Advantages of Palm Print Biometrics

Since the palm area is much larger so that more distinctive features

can be captured and compared to finger prints. This makes it much more

suitable in the process of identification systems than finger prints.

Advantages of using the palm

In addition to the palm, vein authentication can be done using the

vascular pattern on the back of the hand or a finger. However, the pattern in

the PV is most complex and covers the widest area. Due to the palm has

13

no hair, it is easier to photograph its vascular pattern. The palm also has no

sufficient variations in skin color compared with fingers or the back of the

hand, where the color can darken in specific areas.

There are two methods of photographing veins: reflection and

transmission. Fujitsu implements the reflection method. The reflection

process illuminates the palm and photographs the light that is reflected back

from the palm, while photographs light of the transmission method passes

straight through the hand. Both methods capture the nearby infrared light

given off by the region used for identification after diffusion through the

hand. Such an important difference between the reflection method and

transmission method is how they respond to changes in the hand‘s light

transmittance. When the body gets cool due to a low ambient

temperature, the blood vessels in particular capillaries decreasing the flow

of blood throughout the body. This suits up the hand‘s light transmittance,

so light passes through it in much more easier way. If the transmittance is on

the higher end, the hand can become organic molecule with light and light

can easily pass through the hand. In the transmission process, this yields

results in a lighter, less-contrasted image in which vessels are difficult to

see. However, a higher level of light transmittance does not significantly

14

affect the level or contrast of the reflected light. Therefore as a result,

with the reflection method, the vessels can much more easily be seen even

when the hand/body is cool.

1.6 Disadvantages of Palm print Biometrics

The palm print scanners are generally bigger in size and expensive

since they need to capture a larger area than the finger prints scanners.

1.7 PV Patterns

Blood veins are formed during the first eight weeks of gestation in a

chaotic manner, influenced by the environment like mother‘s womb. This is

why VP is identical to each individual, even twins. Vein growth is

associated with a person‘s skeleton, and while capillary arrangement

continue to grow and change, vascular patterns are formed during birth

and do not change over the course of one‘s lifetime.

To scan the veins, an individual‘s hand is placed on the hand guide

(the plastic casing of the scanner device) and the VP is captured by lighting

the hand with near-infrared light. Veins consist of deoxidized

hemoglobin, an iron-containing coloring matter (pigment) in the blood

that carries oxygen throughout the body. These pigments absorb the near IR

light and reduce the reflection rate causing the veins to appear as a black

15

pattern. An every individual‘s scanned PV data (biometric template) is

encrypted for a protection and registered along with the other details in

his/her profile as a reference for future comparison.

1.8 PV authentication Technology

PV authentication is performed according to the comparison

performed between various patterns of human PV. The PVs are the lines

appear in the palm image with the blue lines and such patterns are extracted

and stored in the PV data base. Vascular patterns are generis to each and

every individual, according to Fujitsu research — even identical twins have

different patterns. And since the vascular patterns on the body exist inside,

they cannot be duplicated by means of photography, voice recording or

pattern of finger prints, thereby making this procedure of biometric

authentication more secure than others.

1.9 Principles of Vascular Pattern Authentication

Hemoglobin in the blood is oxygenated in the lungs and carries

oxygen to the tissues of the body through the arteries. After it gets release

its oxygen to the tissues, the deoxidized hemoglobin backs to the heart

through the veins. These two kinds of hemoglobin have distant rates of

absorbency.

16

Deoxidized hemoglobin absorbs light at a wavelength of about 760

nm in the near-infrared region. When the palm gets illuminated with near IR

light, unlikely images can be seen by the human‘s eye, the deoxidized

hemoglobin in the PVs absorb light, hence reflection rate gets reduced and

causing the veins to appear as a black pattern, based on this principle the

region used for authentication on vein is photographed with near-infrared.

Using image processing [Figure 1.2], light and the VP is extracted

and registered. The VP of the person being authenticated is then verified

against the preregistered pattern.

Figure 1.2 PV Patterns

17

1.10 Applications of Biometric Systems

In spite of product development for financial solutions financial

damage is caused by fraudulent withdrawals of money using identity

spoofing with fake bankcards has been fast increasing in last years, and this

has emerged as a significant problem in society. Therefore, rapid increase in

the number of lawsuits filed by victims of identity theft against financial

institutions for their failure to control information to be used only for

personal identification. ―Protection of Personal Information legal Act‖ force

into effect in Japan on 1st May 2005.

Financial institutions have been focusing on biometric authentication

together with IC (smart) cards as a way to reinforce the security of personal

identification. Vein authentication always providing two kinds of systems

for financial solutions, depending on the registered VPs are stored. In one

method, the VPs are stored on the server of a client-server system. The

benefits of this system are that it provides an integrated capability for

managing VPs and comparison processing. In the other type, a user‘s VP is

stored on an integrated circuit card, which is beneficial because users can

control access to their own VP. Suruga Bank uses the server type for their

18

financial solutions, and Tokyo-Mitsubishi is the bank uses the integrated

circuit card system.

Figure 1.3 ATM with PV Recognition System

In July 2004, to ensure customer security, Suruga Bank introduced its

―Bio-Security Deposit‖ — the world‘s first financial service to use Palm

Secure (Figure 1.3). These kinds of services provide high security for

customers using vein authentication, does not require a verification proof

like bankcard or passbook which are used to prevent withdrawals from

branches other than the registered branch and ATMs, hence as a result

minimizing the risk of fraudulent withdrawals. To open a Deposit

account with Bio-Security features, customers go to a bank and have their

PVs photographed at the counter. In order to make sure about the secure

data management, the PV data is stored only on the vein database server at

the branch office where the account is opened.

19

Figure 1.4 ATM with Small PV authentication System

In October 2004, The Bank of Tokyo-Mitsubishi4 launched its

―Super-Integrated Circuit Card Tokyo Mitsubishi VISA.‖ These types of

cards always have the functions of a bankcard, credit card, electronic money

and PV authentication. From a technical prospect and user-friendly point of

view, Tokyo - Mitsubishi Bank narrowed the biometric authentication

methods suitable for financial transactions to PVs, finger veins and finger

prints. The bank then mailed a feedback form to 1,000 customers

and surveyed an additional 1,000 (Thousand) customers who used the

devices in their branches. At the final stage, the bank decided to employ

Palm Secure because the technology was supported by the largest number

of people in the questionnaire. The Super-Integrated Circuit Card contains

the customer‘s PV data and vein authentication algorithms combines

and performs vein authentication by itself. This system is beneficial

because the customer‘s information is not stored at the bank.When a

20

customer applies for a Super-Integrated Circuit card, the banker sends

the card to address of the customer‘s home. To activate the PV

authentication function, the customer brings the verification card and his or

her passbook and seal to the bank counter, where the customer‘s

required vein information is registered on the card. After registration

process, the customer can make transactions at that branch‘s counter

and any ATM (Figure 1.4) using PV authentication and a matching PIN

number.

The Hiroshima Bank started this type of service in date of April 2005,

followed by The Bank of Ikeda6 in date of June 2005. Other

financial institutions, including The Nanto Bank, planned and organized

to start similar services during fiscal 2005.

In 2006, Fujitsu reduced the Palm Secure sensor to 1/4 of its current

size for its next generation product. By using a sensor on existing ATMs,

there will be room or place on the operating panel for a sensor for Felicia

mobiles, a 10-key pad that meets the Data Encryption Standard (DES), as

well as other devices including electronic calculator. The downsized sensor

can also be installed on ATMs in convenience stores.

21

In addition to product development for financial solutions, Fujitsu has

initiated to develop product applications for the general market. Two

products moving faster are in great demand on the general market place.

One product is for a physical access control unit that uses Palm Secure to

protect entrances and exits, and the other product is nothing but a logical

access control unit that uses Palm Secure to protect input and output of

electronic data.

Palm Secure units are used to control access to places containing

systems or machines that manage personal or other confidential and more

secured information, such as machine rooms in companies and outsourcing

centers where important customer data is kept.

Figure 1.5 PV across Control Unit

Due to increasing concerns about security, some commercial sectors

and homes have started using this system to enhance security and safety in a

day to day life. Considering both of these applications, the combined

22

form of the following advantages provides the optimum system: a hygienic

and contactless unit ideal for use in public places, simple user-friendly

operation that requires the user to simply hold a palm over the sensor, and

an authentication mechanism that provides impersonation difficult. The PV

authentication login unit controls access to electronically stored information

(Figure 1.5). When considering the units for financial solutions, there are

two types as follows: a server type and an Integrated Circuit card type.

Becausethe Palm Secure login unit can also be used for authentication

using conventional IDs and passwords, existing operating systems and

available applications can continue to be used. It is also possible to

develop the unit into an existing application to enhance operability. In

the initial stage of introduction, the units are having limitations like

areas of businesses handling personal information that came under the

―Protection of Personal Information Act‖ enforced in the date of April 2005.

However, usage of the units is now expanding to leading-edge businesses

that handle confidential information.

1.11 Authentication with PV Images

Unlike the previous vein feature based authentication mechanism, the

vein code based authentication mechanism extracts the feature codes from

23

palm vein imagery which is represented in binary way. The binarized data

set improves the efficiency of authentication as well as increases the speed

of authentication. From the single piece of PV image there are number of

feature codes can be generated and converted in to binary form. The

generated binary form data can be applied to various problems and can

provide uninterrupted service to many areas. The modern technology

extends the application and scope of PV authentication mechanism which

enables the possibility of using biometric authentication mechanism in a

dense organization.

The application of PV feature based authentication mechanism is

growing in day by day which uses biometric features and the feature is very

much unique for any person. This improves the essential secure storage of

bio features which can be shared between many application.

1.12 Details of Technology

PV image normalization technique

The PV image has to be normalized before being used to perform PV

authentication. The normalization method must be more efficient so that the

features of PV could be maintained. To perform such efficient

normalization, the Fujitsu Laboratories has developed an efficient method

24

which uses contour information to maintain the sh ape and position in PV

images. The image captured by the device attached is verified for the

position and shape and the method removes the distortion from the image

obtained.

Feature code extraction technology

The method uses a different size of feature code which is in the size of

2048 bits, a 2 byte style. The method first generates a sectional image which

fixed size and splits the image into number of regions. For each region the

method extract the features and according to the amount of information

present in the regional image, a 2 byte information or code is framed. The

generation of 2 byte information is done by compressing the information

present in the region. The region based approach enables the

identification of micro changes in the VPs which can be introduced at

different shapes and positions.

1.13 PV Acquisition Methods

There are many ways to snapshoot the PV but each differs with the

accuracy of the vein image.

25

High-Speed Image-Capture Technology for PV Biometric

authentication

Fujitsu Lab developed a prototype authentication device that

employs a high-speed shutter to capture images of the PVs without

blurring even when the palm is in motion, in contrast to the previous

version which captured images when the palm was suspended above the

sensor.

An improved PV identification based on thermal PV image

The infrared PV image is captured using the infrared waves and the

image is stored for processing.

1.14 Organization of the Thesis

This thesis is organized into seven chapters. Chapter 1 gives the

introduction to thesis with Biometrics concepts and palm prints for the

research work. Chapter 2 describes the literature survey related to

palm prints, Algorithms and Methods. Chapter 3 deals with the PV

recognition system using local binary pattern and Gabor filter using

Clahe based contrast enhancement method. In Chapter 4, the work is

Multi – Variant Volumetric Measure on Upper Extremity VP Based PV

Recognition Using Wavelet Transform.

26

Chapter 5 focuses on the Multi-Level Dorsal-Deep VP Based

PV authentication Using Wavelet Transform while Chapter 6 is

devoted to Results and Discussion and Chapter 7 forms the conclusion and

future work envisages respectively.

1.15 Objectives

Biometrics such as PVs, palm prints, face recognition and iris

identification have been extensively used in a lot of employments together

with entry admission management, human being authentication for

computers, online banking, ATMs and foreign Transaction

managements. PV identification uses the exclusive prototypes of PVs to

recognize the persons at a sky-scraping stage of precision. This

work offers two approaches for the development of PV authentication

technology namely A multi variant volumetric measure to perform PV

recognition and A multi- level dorsal-deep VP based PV recognition

approach Both the methods show very high accuracy and also less

processing time.

1. To study and apply appropriate image segmentation technique on VP.

2. To measure VP based PV recognition using wavelet transform.

27

3. To identify features present in PV matrix which is used to compute the

Junctional volume and special volume to find the trustworthy measure of

the PV given.

4. To achieve a better accuracy and low False Acceptance Rate (FAR).

5. To achieve less Processing Time compared to existing methods.

28

CHAPTER 2

LITERATURE SURVEY

The extended research of palm print has been done in many years and

there has been various methodologies have been proposed for the

identification of palm print of a particular person in this era. Some of the

unique methodologies here and their effectiveness and default with many

characteristics are explored here.

Junichi Hashimoto, 2006, discussed VPs based biometric

authentication approach for biometric authentication. Secured Smart Card

Using PV Biometric On-Card-Process [1], discuss the PV biometric

system and its compatibility in financial sector, software design for on-card-

processing solution based on Java virtual machine.

In both the paper the solution is stimulated and the result obtained

and that was tested on PC login application. This increases the tampering of

forgery and increases the quality of authentication. The security level of PV

Biometric On-Card-Process [1], is highly reliable since the FRR and FAR

is very low compared to other biometric systems.

A PCA based PV authentication system is presented in [3], which

uses the Princple component analysis method to perform feature extraction

29

and Yuhang Ding, Dayan Zhuang and Kejun Wang, July 2005[4], analyzed

the difficulty of hand vein recognition and propose a thresol segmentation

with thinning approach.

To capture the palm vein image the infrared camera is used and by

using the PCA approach the extracted features are converted into feature

vector. Both the papers using the highest information of varying size, the

pattern matching is performed to find out the best match from the data base

to perform authentication. The method extracts the edge and junction points

and then performs pattern matching to compute the distance. Based on

computed distance the method performs biometric authentication.

In combination of [3] and [4], there exist Shi Zhao, Yiding Wang

and Yunhong Wang, proposed [5] , uses a hand dorsa to extract the edge

features of palm vein to perform biometric authentication. The method

replaces the necessary of using high quality devices and reduces the cost of

biometric authentication.

In combination with above study Yi-Bo Zhang et al [16] , discusses a

palm vein authentication mechanism which also captures the palm vein

image through the infrared palm image capturing device and then it

identifies the region of interest. Once the region is being identified then the

method extracts the palm vein pattern which is obtained by applying multi

30

level filtering technique. Finally the extracted pattern is performed with

patten matching to perform biometric authentication.

Masaki Watanabe, Toshio Endoh, MoritoShiohara, and Shigeru [6]

and Shani Sarkar et al [12], performs a detailed inspection on the palm vein

authentication mechanisms presented earlier which uses the vessel pattern

of person hands. The growth of biometric authentication has great influence

on various sectors person identification in banks, markets and more.

Paper [6] have shown a biometric authentication using contactless PV

authentication device that uses blood vessel patterns as a personal

identifying factor. Implementation of these contactless identification

systems enables applications in public places or in environments where

hygiene standards are required, such as in medical applications. In addition,

sufficient consideration was given to individuals who are reluctant to come

into direct contact with publicly used devices.

Proposed Multi-Modal PVs-Face Biometric authentication [9],

presents a multimodal PVs and face biometric verification system which

enhances the quality of biometrtic authentication by extracting palm vein

and facial features.

The method combines both PV and facial features to perform

biometric authentication which utilize different methods like RLM( Run

31

length matrix), GLCM (gray level co-occurrence matrix), SF (Statistical

Features), and Moment Invariants (MIs). As a improment of above [9],

Muhammad Imran Razzak et al [11] is developed and it uses a combined

face and finder model based biometric authentication mechanism.

The method uses the multi level fusion approach to extract the

patterns of face and finger veins. Based on the finger vein patterns and face

features the method computes the similarity to perform biometric

authentication. The method uses fuzzy based method to perform biometric

authentication.

Yingbo Zhou and Ajay Kumar [15] discussed two different palm vein

authentication approach which uses hessian phase details which are

extracted from the human vascular patterns from the palm vein images. In

the second method, they used orientation patterns of palm vein lines based

on radon transform.

Junction Point based person identification [18], is proposed which

uses a Junction Point which is defined as the intersection point of the three

or more line segments and a fast JP detector is proposed. In addition to the

study [18], Palm print texture analysis based on low-resolution images

for personal authentication [25], proposes a new branch of biometric

approach - palm print technology, whereby the lines and points can be

32

extracted from our palm for personal authentication- was proposed several

years ago. Study [18] focuses a new feature extraction method based on

low-resolution palm print images and a 2-D Gabor filter is used to obtain

the texture information and two palm print images are compared in term of

their hamming distance.

Junction Point based person identification [18], make Junction Points

of the palm print and PV line segments associated with their

directions of palm print and PV are computed. Transition number is used to

detect the junction function.

The edge segments are thinned using a morphological operation.

Then centre pixel within a 3×3 neighborhood, which is a junction that is

tested. In this paper, they proposed recognition approach by combining

palm print and PV at the feature level.

A novel feature, Junction Point (JP), which is obtained on the fused

line segments images are proposed. JP features have been verified as a

more compact and accurate representation of palm images.

A performance Evaluation of Shape and Texture based methods for

vein recognition [19], is improved from above [18] on the basis to give fair

comparisons of shape and texture based methods for vein recognition.

The shape of the back of hand contains information that is capable of

33

authenticating the identity of an individual. In this paper, two kinds of shape

matching method are used, which are based on Hausdorff distance and Line

Edge Mapping (LEM) methods. The vein image also contains valuable

texture information, and Gabor wavelet is exploited to extract the

discriminative feature.

The edge methodology is also applied in Person recognition by

fusing palm print and PV images based on Laplacian palm representation

[39], combines both palm print and palm vein features to perform person

recognition. Both palm print and palm ven images are applied through the

edge preserving algorithm and then the contrast of the image is enhanced

through wavelet fusion technique. The extracted features are represented by

locality preserving projection to perform matching process.

Biometric Verification Using Thermal Images of Palm Dosra VPs

[20], discuss a biometric authentication mechanism which uses the palm

vein pattern extracted from the thermal images.

The method does not require any prior knowledge about the objects.

The method uses an the IR camera is incorporated to capture the thermal

images. Once the method captures the input image then the region of

interest is identified and feature vein patterns are extracted to perform palm

vein authentication.

34

The method uses watershed segmentation and multi level filtering to

extract the vein patterns to improve the performance of the biometric

authentication.

Coherence-enhancing Diffusion Filtering‖, International Journal of

Computer Vision [21], present a multi scale method in which a nonlinear

diffusion filter is steered by the so-called interest operator (second-moment

matrix, structure tensor). An m-dimensional formulation of this method is

analyzed with respect scale-space properties.

An efficient scheme is presented which uses a stabilization by a

semi-implicit additive operator splitting (AOS), and the scale-space

behavior of this method is illustrated by applying it to both 2-D and 3-D

images.

Image Enhancement and Desnoising by Complex Diffusion Processes

[22], present an biometric authentication method which uses linear and non

linear methods using real valued diffusion equations. The method proves

that the the method generates secondary smooth derivative.

In [23] Guy Gilboa, NirSochen and Y.Yehoshua Zeevi analyze and

prove some properties of coupled shock and diffusion processes. Finally an

original solution of adding a complex diffusion term to the shock equation

35

is proposed. This new term is used to smooth out noise and indicate

inflection points simultaneously. The imaginary value, which is an

approximated smoothed second derivative scaled by time, is used to control

the process. This results in a robust deblurring process that performs well

also on noisy signals.

In [24] SuleymanMalki, Yu Fuqiang and Lambert Spaanenburg and

Biometric Recognition: Security and Privacy Concerns [26], discusses the

biometric identification which is an important security application that

requires non-intrusive capture and real-time processing.

Security systems based on finger prints and retina patterns have been

widely developed, but can be easily falsified. Recently, identification by

VPs has been suggested as a promising alternative. The method uses the

available feature extraction approach to improve the performance of palm

vein authentication.

The biometrics which offers greater security and convenience than

traditional methods of personal recognition. In some applications,

biometrics can replace or supplement the existing technology. In others, it

is the only viable approach.

Feature Level Fusion of PVs and Signature Biometrics [32],

discusses the traditional biometric systems that based on single

36

biometric usually suffer from problems like imposters' attack or hacking,

unacceptable error rate and low performance. So, the need of using

multimodal biometric system occurred .In this paper, a study of multimodal

PVs and signature identification is presented.

Features of both modalities are extracted by using morphological

operations and Scale Invariant Features Transform (SIFT) algorithm and a

comparison for both methods is developed. Feature level fusion for both

modalities is achieved by using a simple sum rule. Fused features vectors

are subjected to Discrete Cosine Transform (DCT) to reduce their

dimensionalities.

In Vascular Pattern Analysis towards Pervasive PV authentication

[33], infrared images are used to perform biometric authentication which is

sent through three different levels. These infrared images are also discussed

in [18], [19] and [25].

First the image is pass through the vascular pattern marker to perform

marking and then feature is extractd using the VPEA (vascular pattern

extractor algorithm). Third the extracted feature is passed through vascular

pattern thinning algorithm.

The final image is indexed to the data set to perform biometric

authentication. Infrared camera is also utilized in PV extraction and

37

matching for personal authentication [35], performs palm vein

authentication based on the image captured through the infrared camera.

First theimage is smoothed and then the features are extracted by

identifying the region of interest. The feature extraction is performed by the

multi level scale filtering and finally matching is performed.

Human Identification Using Palm-Vein Images [34], discuss

variety of approaches for biometric palm vein authentication problem. The

proposed approach attempts to more effectively accommodate the potential

deformations, rotational and translational changes by encoding the

orientation preserving features and utilizing a novel region-based matching

scheme.

Curve let-based PV biometric recognition [36], presents a novel

personal recognition system utilizing palm VPs and a novel technique to

analyze these VPs. The technique utilizes the curve let transform to extract

features from VPs to facilitate recognition.

This technique provides optimally sparse representations of objects

along the edges. Principal component analysis (PCA) is applied on curve

let-decomposed images for dimensionality reduction. A simple distance-

based classifier, such as the nearest-neighbor (NN) classifier, is employed.

PV Verification System based on SIFT matching [37], presents

38

a novel personal recognition system utilizing palm VPs and a novel

technique to analyze these VPs. The technique utilizes the curve let

transform to extract features from VPs to facilitate recognition.

This technique provides optimally sparse representations of objects

along the edges. Principal component analysis (PCA) is applied on curve

let-decomposed images for dimensionality reduction. A simple distance-

based classifier, such as the nearest-neighbor (NN) classifier, is employed.

The experiments are performed using the PV database. Experimental

results show that the algorithm reaches a recognition accuracy of 99.6% on

the database of 500 distinct subjects.

Multispectral palm image fusion for accurate contact-free palm print

recognition [40], propose to improve the verification performance of a

contract-free palm print recognition system by means of feature- level

image registration and pixel-level fusion of multi-spectral palm images. The

method involves image acquisition via a dedicated device under contact-

free and multi-spectral environment, preprocessing to locate region of

interest (ROI) from each individual hand images, feature-level registration

to align ROIs from different spectral images in one sequence and fusion to

combine images from multiple spectra. The advantages of the

proposed method include better hygiene and higher verification

39

performance.

Biometric identification is an important security application that

requires non-intrusive capture and real-time processing. Security systems

based on finger prints and retina patterns have been widely developed, but

can be easily falsified. Recently, identification by VPs has been suggested

as a promising alternative.

In Vein Feature Extraction Using DT-CNNs [41], an existing

feature extraction algorithm that has been developed for finger print

recognition is adapted for vein recognition.

An improved PV recognition system using multimodal features and

neural network classifier has been developed and presented in an Enhanced

PV Recognition System Using Multi-level Fusion of Multimodal Features

and Adaptive Resonance Theory [42].

Biometric Cryptosystem Involving Two Traits and PV as Key [43],

proposes a scheme which involves an idea of including three biometric

traits of a person where in the sense even if one fails the other trait could be

utilized for verification or identity.

Moreover the concept of cryptosystem is involved, where one of the

biometric trait – the PV itself acts as a key to utilize the stored template

database. The main idea in using one of the biometric traits as a key is

40

that under any circumstance no two PVs match unless it belongs to same

person. It is a valid key which no one can steal or misuse.

Online joint palm print and PV verification [44], discuss a combined

palm print with vein approach and deployed in the web to perform

biometric authentication.

To improve the performance and speed the web based solution is

deployed with the support of dynamic fusion technique which extract the

features of palm print and vein features. The extracted feature is stored in a

data set to perform matching and the method has produced more speed with

precision.

PV Recognition with Local Binary Patterns and Local Derivative

Patterns [45], presents a promising new approach based on local texture

patterns. First, operators and histograms of multi-scale Local Binary

Patterns (LBPs) are investigated in order to identify new

efficient descriptors for palm VPs. Novel higher-order local pattern

descriptors based on Local Derivative Pattern (LDP) histograms are then

investigated for PV description.

Both feature extraction methods are compared and evaluated in the

framework of verification and identification tasks. Extensive experiments

on CASIA Multi-Spectral Palm print Image Database V1.0 (CASIA

41

database) identify the LBP and LDP descriptors which are better adapted to

PV texture. Tests on the CASIA datasets also show that the best adapted

LDP descriptors consistently outperform their LBP counterparts in both PV

verification and identification

Biometric Verification Using Thermal Images of Palm Dosra VPs

[48], uses palm dorsa infrared image as input which is captured using the

infrared camera. In the second stage the method extract the features and the

input captured image is submitted to the web server where the extraction

and matching is performed.

Thermo graphic imaging of the subcutaneous vascular network of the

back of the hand for biometric identification [50], describes research that

has been undertaken by the authors to use the subcutaneous vascular

network (VP) of the back of the hand as a unique personal biometric for

identification.

An outline will be given of a prototype low cost automatic thermo

graphics imaging system which has been developed by the authors to obtain

VPs for positive identification. The paper includes consideration of the

image acquisition, image processing and VP matching strategies. A

summary of experimental results concerning the acceptance and rejection

rates for the system is also provided.

42

Improved VP extracting algorithm and its implementation [51],

proposes an improved VP extracting algorithm which compensates the loss

of VPs in the edge area, gives more enhanced and stabilized VP

information, and shows better performance than the existing algorithm.

It is made as an improment to study [35] and also, the problem arising from

the iterative nature of the filtering preprocess in the existing algorithm is

solved by designing a filter that is processed only one time so that a fast

recognition speed and reduced hardware complexity is obtained.

For further enhancement noise removal is introduced in A biometric

identification system by extracting hand VPs [52], uses an new filter which

shift and add the binary values to perform noise removal which can be

performed at any point of the image obtained. The method also uses the

FPGA (field programmable gate array) device to speedup the recognition

process.

Illumination invariant face recognition using near-infrared image

[54], present a novel solution for illumination invariant face recognition for

indoor, cooperative-user applications.

First, an active near infrared (NIR) imaging system that is able to

produce face images of good condition regardless of visible lights in the

environment is presented. Second, the resulting face images encode

43

intrinsic information of the face, subject only to a monotonic transform in

the gray tone is showed; based on this, local binary pattern (LBP) features

to compensate for the monotonic transform, thus deriving an illumination

invariant face representation is used. Then, methods for face recognition

using NIR images, statistical learning algorithms are used to extract

most discriminative features from a large pool of invariant LBP features and

construct a highly accurate face matching engine is presented.

Finally, a system that is able to achieve accurate and fast face

recognition in practice, in which a method is provided to deal with specular

reflections of active NIR lights on eyeglasses, a critical issue in active NIR

image-based face recognition is introduced.

Extensive, comparative results are provided to evaluate the imaging

hardware, the face and eye detection algorithms, and the face recognition

algorithms and systems, with respect to various factors, including

illumination, eyeglasses, time lapse, and ethnic groups.

Biometric authentication by hand VPs [55], discuss as the hand VPs

are unique and universal. VP is used as biometric feature in recent years.

But, it is not very much popular biometric system as compared to other

systems like finger print, iris etc, because of the higher cost. For

conventional algorithm, it is necessary to use high quality images, which

44

demand high-priced collection devices. There are two approaches for vein

authentication, these are hand dorsa and hand ventral. Currently the work

going on hand dorsa VPs.

Here the new approach for low cost hand dorsa VP acquisition using

low cost device and proposing a algorithm to extract features from these

low quality images is introduced.

Knuckle profile identity verification system [56], proposes a system

(method and apparatus) for verifying/identifying a person based on contour

of the knuckle surface of at least one hand, e.g. a digitized waveform unique

to each individual.

At an Enrollment Station a microcomputer is connected to a device

for identifying the knuckle surface profile (e.g. a video camera or

electromechanical contour sensing device). A candidate user grasps a grip

handle, preferably vertically oriented, on the apparatus, positioning a fist

before a viewing window and activating the device to scan or assess the fist

and generate a contour of the user's knuckle surface contour. User's data

comprises a knuckle contour, an assigned PIN, and optionally, information

such as user's name, bank ID number, Social Security Number, and access

restrictions.

45

User's data may be stored as a profile (template) in an ID card, and/or

in a master database containing data of all authorized users in a guarded

system.

Hand vein recognition based on multi supplemental features of multi-

classifier fusion decision [61], proposes a new algorithm based on multi

supplemental features of multi-classifier fusion decision is proposed. It

overcomes the disadvantages of the single feature recognition. Experimental

results indicate that the proposed methods can significantly improve the

recognition accuracy and reliability compared to the previous hand vein

recognition.

Near- and Far-Infrared imaging for VP biometric [62], compares two

different imaging techniques like near infrared and far one to the purpose of

extracting palm vein patterns. The paper also performs deep analysis on

these approaches using varying number of samples.

Minutiae Feature Analysis for Infrared Hand VP Biometrics [69] and

clustering the Dorsal Hand VPs using the Firefly Algorithm [98], proposes

a novel technique to analyze the infrared VPs in the back of the hand for

biometric purposes. The technique utilizes the minutiae features extracted

from the VPs for recognition, which include bifurcation points and ending

points.First, the images of blood vessels on back of the hands of people is

46

analyzed, and after pre-processing of images and feature extraction (in the

intersection between the vessels) to identify people firefly clustering

algorithms is used.

This identification is done based on the distance patterns between

crossing vessels and their matching place. The identification will be done

based on the classification of each part of NCUT data set and it consisting

of 2040 dorsal hand vein images.

High speed in patterns recognition and less computation are the

advantages of this method. Similar to finger prints, these feature points are

used as a geometric representation of the shape of VPs. Analysis of a

database of infrared VPs shows a trend that for each hand VP image, there

are, on average, 13 minutiae points in each VP image, including 7

bifurcation and 6 ending points. The modified Hausdorff distance algorithm

is proposed to evaluate the discriminating power of these minutiae for

person verification purposes.

Experimental results show the algorithm reaches 0% of equal

error rate (EER) on the database of 47 distinct subjects, which indicates the

minutiae features of the VP can be used to perform personal verification

tasks. The paper also presents the preprocessing techniques to obtain the

minutiae points as well as in-depth study on their tolerance to processing

47

errors, such as loss of features and geometrical displacement.

Physiology based face recognition in the thermal infrared spectrum

[70], present a novel framework for face recognition based on physiological

information. The motivation behind this effort is to capitalize on the

permanency of innate characteristics that are under the skin.

To establish feasibility, a specific methodology to capture facial

physiological patterns using the bio heat information contained in thermal

imagery is proposed. First, the algorithm delineates the human face from the

background using the Bayesian framework. Then, it localizes the superficial

blood vessel network using image morphology. The extracted vascular

network produces contour shapes that are characteristic to each

individual.

The branching points of the skeletonized vascular network are

referred to as thermal minutia points (TMPs) and constitute the feature

database. To render the method robust to facial pose variations, for each

subject to be stored in the database five different pose images are collected.

During the classification stage, the algorithm first estimates the pose of

the test image. Then, it matches the local and global TMP structures

extracted from the test image with those of the corresponding pose images

in the database.

48

Optical imaging and parametric characterization of frostbite changes

in human hand tissues [74], deals with the optical characterization of

frostbite of the hands, which involves the skin and tissue structure beneath

it and is carried out by laser reflectometry and Monte Carlo simulation. The

grid of the dorsal side of the hand is developed on a computer monitor and

the same is scanned point-to-point.

Data obtained in the form of normalized back-scattered intensity

(NBI), after interpolation and median filtering are displayed as color-coded

images. In controls the NBI is significantly higher at the abductor brevis

muscle and minimum at the tendon of the flexor digitorum, pollicislongus

and at the nails compared to that at the other regions.

Due to frostbite the NBI values are lower at various locations in the

fingers and dorsal sites in these subjects compared to those of the respective

controls. The variation in NBI is maximum for the first probe compared to

the other probes.

A Novel Biometric system for Person Recognition Using PV Images

[85], conducts a comprehensive comparative study of three local invariant

feature extraction algorithms: Scale Invariant Feature Transform (SIFT),

Speeded-Up Robust Features (SURF) and Affine-SIFT (ASIFT) for PV

recognition. First, the images were preprocessed through histogram

49

equalization, then three algorithms were used to extract local features, and

finally the results were obtained by comparing the Euclidean distance.

Experiments show that they achieve good performances on our own

database and multispectral palm print database.

In Enhanced PV Recognition System Using Multi-level Fusion of

Multimodal Features and Adaptive Resonance Theory [88], uses shape and

texture features have been extracted and multimodal features have been

obtained at feature extraction level as well as matching score level.

The Neural network classifier has been used to classify the VPs for

making necessary decision.

It is concluded from the analysis that the multimodal PV recognition

system provides better performance compared uni modal features.

PV recognition using curve let transform [87], technique utilizes the

curve let transform to extract curve-like features from VPs and provides

optimally sparse representations of the patterns. For evaluation, HK Poly

University multispectral palm print database is used.

Contactless PV identification using multiple representations [2],

investigates some promising approaches for the automated personal

identification using contactless PV imaging (from which sudy [95] made as

an advancement proposed paper). First they present two new PV

50

representations, using Hessian phase information from the

enhanced vascular patterns in the normalized images and secondly

from the orientation encoding of PV line-like patterns using

localized Radon transform.

The comparison and combination of these two PV feature

representations, along with others in the PV literature, is presented for the

contactless PV identification. In addition to the contactless approach of PV

identification, Contactless PV Identification using Multiple Representations

[95], investigates some promising approaches for the automated personal

identification using contactless PV imaging. The method uses different

representation of palm vein features one in form of hessian matrix and

another in form of line like patterns.

The comparison and combination of these two PV feature

representations, along with others in the PV literature, is presented for the

contactless PV identification. Also evaluate the performance from various

PV representations when the numbers of training samples are varied from

minimum.

The recognition rate of this method is more accurate and the error is

less than one percent. At the end the correctness percentage of this method

for identification is compared with other various algorithms, and the

51

superiority of the proposed method is proved.

Problem Statement Biometrics presented techniques for

authenticating group of people by using bodily personality features. A

variety of biometric identification methods such as PV, facial recognition,

finger print, iris etc,

1. These conservative schemes contain some difficulties in conditions

of feasibility and arrangement in palm print and finger print identification

schemes, customers encompass to handle the outer of the contribution

antenna by their palm and finger.

2. This could be based on a large amount of problems for the

customer and it was too probable to take dormant in sequence from the PV

antenna.

3. In adding together, the state of the palm outside and coat bend can

cause humiliated identification precision.

4. In facial identification, presentations extremely depended on

face appearances and enlightenments which can modify.

5. Iris identification mainly depends on conditions of precision, but

the detaining tool is luxurious and can be difficult contrasted to further

biometric schemes.

52

Problem Solution

To overcome the identified problems:

1. Vein prototypes such as PVs and hand veins had been considered.

2. PV identification used interior sequence as of a human being‘s

remains and vein prototypes which could be seen with infrared beam

illuminators and a capturing device. As well as, it was tough to get interior

models.

Summary

Biometrics is a method by which a person's authentication

information is generated by digitizing measurements of a physiological or

behavioral characteristic. Biometric authentication checks user's claimed

identity by comparing an encoded value with a stored value of the

concerned biometric characteristic. Various biometric authentications are

face recognition, finger prints, hand geometry etc. Among this, the most

recent technology is PV authentication. Various techniques have been

proposed by researchers in the area of PV identification. Most of the

methods uses various features of PV like geometric, cosine similarity,

wavelet features etc but lags with the accuracy of identification and

authentication. Authentication using hand geometry does not have the same

degree of permanence or individuality as other characteristics. Even

53

authentication using Cosine similarity and wavelet features lags in

accuracy. PV authentication is highly accurate and secure since the

authentication data exists inside the body and it is difficult to forge. It uses

vascular patterns as personal identification data. This paper presents the

analysis of various methods and algorithms that identifies the VPs in palm

for authentication purpose.

Table 2.1 Analysis of Various Techniques

Methods Results Limitation

1.Using multiscale

wavelet edge detection

Efficiency is 100%. Sensitive to noise,

discriminating between

edges

2.Using adaptive

Gabor filter

EER is 0.6%,High

accuracy and speed

Better using directional

filter bank method

3.Using Hybrid

Principal Component

EER was 9.839% for

unscaled PCA-ANN

EER has lower pixels

resolution (46.37) for

scaled PCA-ANN

Literature presents the various techniques for PV recognition. In this

conventional edge detection techniques suffer from limitation like

sensitivity to noise, discriminating between edges etc. the table clearly

shows that PV recognition using neural network is quite efficient and

accurate.

54

CHAPTER 3

PV RECOGNITION SYSTEM USING LOCAL BINARY PATTERN

AND GABOR FILTER USING CLAHE BASED CONTRAST

ENHANCEMENT METHOD

3.1 Introduction

Biometrics refers to methods for recognizing people by using

physical human features. There have been several kinds of biometric

recognition systems such as PV identification, face identification, iris

identification, finger print identification etc. However, these conservative

systems have some difficulties in conditions of expediency and

presentation.

In PV and finger print recognition systems, users have to touch the

outside of the input sensor by their palm and finger. This can cause a large

amount of problems for the user and it is also probable to take dormant

information from the PV sensor.

In consideration, the state of the palm surface (e.g. sweat, dryness)

and skin bend can cause humiliated identification accuracy. For face

recognition, presentation extremely depends on facial expressions and

enlightenments, which can be altered. Iris identification is most

55

dependable in conditions of accuracy, but the capturing tool is luxurious and

can be inconvenient contrasted to further biometric systems.

To rise above these problems, VPs such as PVs [13] and hand veins

have been studied [14]. PV recognition uses interior information as of a

person‘s body and VPs, which can be seen with IR (Infrared)

illuminators and a camera. Moreover, it is hard to pick up internal patterns.

However, the finger print and hand vein recognition tool is presently too

large compared to PV recognition systems.

3.2 Previous Research

Yanagawa‘s study established that PV prototypes could be

appropriately used for individual classification [1]. They demonstrated that

the palm of every human being has completely dissimilar VPs and a finger

vein outline shows as much degrees of freedom as iris patterns [1]. Miura

proposed a method of removing palm VPs by using frequent line tracking

from a variety of initial places. There proved to be high-quality extraction of

presentation with look upon to image shading [2]. These researchers used

678 PV images for identification, while the accuracy was 85% and

the processing time was 10mille seconds [2]. Zhang also proposed a

removal technique based on the curvelet information of the shape of PV

56

images and nearby unified planned neural networks [3]. The proposed

neural networks were qualified using information from true and false

VP (Vein Pattern) regions [3]. These researchers used exactly 3200 PV

images for matching while the accuracy was 88% [3]. In the majority of

current study, Miura showed that the thickness of palm VPs could

change in palms under dissimilar weather situations (shown by using 678

PV images with an accuracy of 90%) [4]. Miura proposed palm VP

extraction based on the curvature value on a cross part of a palm VP and

extracted the points with high curvature values in each of four instructions.

This allowed to extracting a palm VP even in the middle of a variety

of pattern thicknesses [4]. Profitable manufactured goods have also been

introduced by Hitachi [15]. In all these PV removal method is used.

However, PV images are not always clear, but can sometimes also show

irregular shadings and highly saturated areas. Therefore, detection errors can

arise when extracting exact vein prototypes. Also, the PV extraction step can

lead to increase of processing time. To overcome these troubles, a new

PV identification system is offered. In this identification system a Local

Binary Pattern method is used for removing local information of PV

outlines. Using thLBP, identification presentation was more consistent

adjacent to irregular shadings and highly flooded sections. In addition, the

57

overall information of PV outlines based on Wavelet transform is removed.

The two attained standards by the LBP and Wavelet transform were

joined by the SVM (Support Vector Machine) and the authentication

performance was much superior.

3.3 Block Diagram

Figure 3.1 Block Diagram of Gabor Filter and Local Binary Pattern

3.4 Methodologies

CLAHE based Contrast Enhancement

Fast Discrete Curvelet Transform

58

Texture Characterization:

Local Binary Pattern – Gabor Filter

Matching: Euclidean Distance

Performance metrics: Sensitivity(recall), quality of being specific,

precision and accuracy

3.5 Input, Selected Region and Results of Combined Features

Figure 3.2 Examples of Input PV Region

59

The figure 3.2 shows the snapshot of selected input image to perform

authentication. A right hand with inclined image which has to be rotated

to get the features of the palm VP is selected.

Figure 3.3 Examples of Selected PV Region

The figure 3.3 shows the snapshot of selected PV region being

extracted from the input image and it shows clearly that the portion

marked in the previous stage has been extracted for further processing.

60

Figure 3.4 VP

The figure 3.4 shows the snapshot of image which is removed from

noise present in the ROI image. It shows that the image has been taken out

from the noise present in the image which shows the Region of interest

image after performing the background subtraction process and

the background subtraction has been performed by binary imaging

technique.

61

The figure 3.4 shows the snapshot of normalized palm VP which

will be used to perform skeleton identification process. The normalized

image has veins with bigger dimensions so that the veins skeleton has

to be identified to perform junction point identification and to perform other

computation process.

Figure 3.5 Curvelet Decomposition Pattern

The figure 3.5 shows the details of Curvelet Decomposition Pattern

points which have been identified from the given input image. Such points

will be used to compute the volumetric measure between the points.

62

Figure 3.6 Gabor Texture Representation Regions

The figure 3.6 shows the snapshot of Gabor Texture Representation

result produced by methodology. It shows clearly that the proposed method

has produced efficient results.

The above discussions have been in presented; the proposed method

with various stages of results. The method has figured out different junction

points from the input image and has computed volumetric measure

between all the junction points. By calculating the volumetric measure

63

between all the measures a cumulative special volume has been computed.

This special volume has been used to compute trustworthy measure of the

given input image to perform authentication of the PV image.

Figure 3.7 LBP Texture Representations

The figure 3.7 shows the snapshot of LBP Texture Representation

result produced by methodology. It shows clearly that the proposed method

has produced efficient results. The method reads the input image and applies

Gabor filter which removes the noise from the image. The noise removed

image is applied with histogram equalization technique which enhances the

64

input image quality. The quality improved image is applied with wavelet

analysis which splits higher and lower signals to produce two

different images where the dorsal VP is in the higher order image and the

lower order image represents the deep VPs. The two images are split

into number of small images and their features are extracted to identify the

junction points. The extracted junction points are stored in a dorsal junction

matrix and deep junction matrix. Based on the two matrixes generated the

dorsal vein depth measure and deep vein depth measures are computed.

Using these two measures a cumulative depth is computed on which the

person identification is performed.

65

Figure 3.8 LBP and Gabor Performance

The figure 3.8 shows the comparison of PV authentication accuracy

produced by different methods. The proposed methods have produced

efficient accuracy than the other methods at different number of classes and

samples. The proposed Texture Characterization Method has 2 approaches

Namely Local Binary Pattern and Gabor Filter. The method removes the

66

noise and performs histogram equalization to enhance the image. The

enhanced image is applied with wavelet analysis and splits the higher order

and lower order VP. Generated two different images are split into sub

sample images and their junction points are identified. Identified junction

point matrix is used to compute the dorsal depth and deep vein depth

measure to compute the cumulative weight. Based on cumulative weight an

average distance measure is computed to identify the person base on some

threshold value. The proposed method has produced efficient results

compared to other methods.

67

Figure 3.9 Individual Samples of Recognition

The figure 3.9 shows a PCA performance graph between number of

samples/subjects and recognition percentage. Recognition percentage

reaches half the level during number of samples are equal to 9.

68

Figure 3.10 Samples Vary with Recognition Percentage – Set 1

The figure 3.10 shows a gabor performance graph between number of

samples/subjects and recognition percentage. Recognition percentage

reaches 60% of the level during number of samples are equal to 9.

69

Figure 3.11 Samples Vary with Recognition Percentage – Set 2

The figure 3.11 shows a LBP performance graph between number of

samples/subjects and recognition percentage. Recognition percentage

reaches 60% of the level during number of samples are equal to 8.

70

Figure 3.12 Comparisons of PCA and Gabor

The figure 3.12 shows a PCA (Vs) gabor performance graph between

number of samples/subjects and recognition percentage. Recognition

percentage of PCA reaches 50% of the level during number of samples are

equal to 9. Recognition percentage of gabor reaches 60% of the level

during number of samples are equal to 9.

71

Figure 3.13 Comparisons of PCA and LBP

The figure 3.13 shows a PCA (Vs) LBP performance graph between

number of samples/subjects and recognition percentage. Recognition

percentage of LBP reaches 60% of the level during number of samples are

equal to 8. Recognition percentage of PCA reaches 40% of the level

during number of samples are equal to 8.

72

Figure 3.14 Comparisons of PCA and Gabor, LBP

The figure 3.14 shows a PCA (Vs) proposed system (gabor+LBP)

performance graph between number of samples/subjects and recognition

percentage. Recognition percentage of PCA reaches 40% of the level during

number of samples are equal to 8. Recognition percentage of proposed

system reaches 70% of the level during number of samples are

73

equal to 8.

Figure 3.15 Comparisons of PCA and Gabor, LBP Version with

Number of Samples

The figure 3.15 shows a LBP (Vs) proposed system (gabor+LBP)

performance graph between number of samples/subjects and recognition

percentage. Recognition percentage of LBP reaches 60% of the level during

74

number of samples are equal to 8. Recognition percentage of proposed

system reaches 70% of the level during number of samples are

equal to 8.

Figure 3.16 Comparison of Other Methods with the Proposed

75

The figure 3.16 shows LBP (Vs) proposed system (gabor+LBP) (Vs)

gabor (Vs) PCA performance graph between number of samples/subjects

and recognition percentage. Recognition percentage of LBP reaches 60% of

the level during number of samples are equal to 8. Recognition percentage

of proposed system reaches 70% of the level during number of samples

are equal to 8. Recognition percentage of PCA reaches 40% of the level

during number of samples are equal to 8. Recognition percentage of gabor

reaches 55% of the level during number of samples are equal to 8.

3.6 Architecture of PV Recognition

The PV images are got from training images and testing images. The

regions of PV images are localized and further process followed in about

stretching and sub-sampling. The PV images are now ready for the

extraction and extraction is carried out in two parallel ways.

1. Extracting PV code using LBP

2. Extracting PV feature values by using wavelet

The calculated humming and Euclidean distance from the above two

extraction process are proceded with SVM classification where 2000 PV

images from database are present. After the resultant verification, the result

may be matching or non-matching.

76

Algorithm steps

Step1: Capturing PV Image from Device

Step2: Localizing the PV Region by using masks

Step3: Stretching and sub-sampling the localized Region

Step4: Extracting the PV code by using LBP (Local Features)

if x≥0, Initialize n=0

if x<0, s (in-ic)^2^n

Step5: Matching the extracted codes with enrolled ones

Step6: Extracting the Wavelet Transformed Features (Global

Features)

Step7: SVM classification by using Hamming Distance and

Euclidean Distance

d‘=μ1-μ2/√σ1^2 +σ2^2/2

step8: Result verification based on the two distance values, like matching

or Non-matching.

77

Figure 3.17 Architecture of PV Recognition

78

3.7 Junction Point (JP) Detection

The junction points are nothing but the connection of two edges. In

PV image there will be number of lines according to the VP of human.

There will be number of edges meet and cross which generates junction

points. Such junctions are generated through the cross and meet of different

veins of human vessels which passes bloods. The biometric authentication

has greater impact of such junction points and in this case the junction points

are more efficient and unique than other features of human biology. By

identifying the junction points of human PV image, the number of points and

edges can be identified and they are unique for each person which can be

used to perform biometric authentication.

The crossings point of the central B is given as below

B=∑^8 n=1 |s (Qn+1)-s (Qn)|, s (Q9) =s (Q1) (1)

Where s (x) is the binary value (0or1)of pixel at x. The centre

point Q is considered to be a junction point if Q is an edge point and if

B>=6

3.8 Cross Correlation on Join Point Extraction

The connection of two joints can be extracted by performing cross

correlation and can be performed using the below equation.

79

Sab=A^i U B^i U/√1|A^i u||Ci||B^i U| (2)

In this equation the value of variable A is predefined and B is the

result obtained. Also the value of Sab is 1 when the authentication is

successful.

3.9 Limiting the PV Region

The PV images are integrated with the shaded sections at both ends in

the flat way. Because of this, the image is corrupted. So shared portions can

only be cut off with fixed pixel sizes at both ends. The outline of each

person‘s palms is dissimilar. So the PV section in order to standardize the

PV figure and take out the surface from the standardized image was

identified. The PV region is more dazzling than the setting area, because

infrared radiance shining throughout the skin. Therefore, in order to contain

the PV portion from captured images, the covering value was calculated

in the Y path for each X point and the place at which the covering

value became maximal was resolute as the border arrangement among

the palm and the setting in the Y path. Figure 3.18 shows the result of

localizing the palm region with masks.

80

Figure 3.18 Examples of Localizing the PV Region with Masks

3.10 Extending and Sub-Sampling the contained Region

The contained PV region in the way of the X and Y axis is

extended. As a result, a 150 × 60 pixel extended image was formed. Then,

in order to develop the dispensation time, the extended figure was converted

to 50 × 20 pixels by averaging the hoary values for every 3 × 3 pixel block.

By using sub-sampled descriptions, the removed PV skin textures became

tough not in favor of noise aspects. The 3 × 3 pixel block was resolute

based on the breadth of the thinnest vein in the extended figure (which was

calculated as 3 points by experimentation).

81

Figure 3.19 Stretched Images of Figure

3.11 Extracting the Palm Vein Code by Using the Local Binary

Pattern

In preceding PV authentication, the technique took a large amount of

dispensation time because it was essential to identify PV areas before

removing the skin textures. So, the technique suffered that authentication

presentation was affected by PV recognition faults. To overcome this

trouble, a Local Binary Pattern technique that could take out the PV

codes in the whole vein area exclusive of requiring exact recognition of that

area is used. Because the LBP technique compares limited sections, it is

robust against high dispersion and asymmetrical shadings in the captured

82

image. The value of the LBP established the LBP operative tool which was

a non-parametric 3 × 3 kernel for quality categorization. The LBP can be

defined as a prearranged set of binary standards resolute by evaluating

the hoary standards of a middle point and the 8 neighborhood region pixels

around the center. The prearranged set of binary values can be uttered in

decimal form as shown by Equation (1).

LBP (xc, yc) = ∑^7 n=0 s (in-ic)^2^n (1)

Where ic and in represent the hoary assessment of the middle point

(xc, yc) and the values of hoary for the 8 adjoining pixels correspondingly.

The function s(x) is defined as follows.

S(x )= 1, if x≥0

0, if x<0 (2)

Figure 3.20 The LBP Operator

83

The figure 3.20 shows about the binary intensity of the LBP Operator.

The binary value of 0111000 is equated with the decimal value 120.

3.12 Matching the Extracted Codes with Enrolled Ones

The proposed PV recognition compute the HD value which is

equated to the extracted codes with the registered ones. The HD is used to

calculate the distinction between any two PVs, as r epresented in

Equation (3):

HD=||(code A * code B) U mask A U mask

B|| / mask A U mask B || (3)

The code A and code B values are the take out from the PV code

vector and the registered one, correspondingly. The mask A and mask B

values are extracted from the control code vector and from the registered

one, respectively.

Authentication presentation is decreased by evaluating the skin

sections of the captured PV image with the skin regions of registered ones.

Therefore, a direct policy to decide whether the extracted PV code was

accessible or not. (If the code extracting location was a vein region, the

control code was 1, whereas, if the code extraction position was a skin

region, the control code was 0 and it was not used for identification.) To

84

decide the types of direct codes of the code extraction position, the standard

deviation of the 3 × 3 block in which LBP extracted code was calculated.

If a vein was included in the 3 × 3 block, the standard deviation was high

because the difference between the gray values of the vein and the skin was

high. The threshold of the standard deviation with the accuracy of

maximized PV recognition is calculated.

3.13 Extracting Wavelet Transformed Features: Global Features

The PV code by LBP represents the local and detail features in 3 ×3

block. To enhance the recognition accuracy, global features by using

Wavelet transform was extracted. There exist many kinds of Wavelet basis

such as Haar, Gabor, Daubechies. Experimental results showed that the

accuracy of PV recognition was smallest in case of using Gabor basis for

Wavelet transform. Optimal frequency and kernel size of Gabor bases were

selected based on the minimum authentication of accuracy with the training

data of 2 000 images. First, multi-resolution decomposition of stretched

and sub-sampled PV region are carried out. From that, four sub regions

were defined: LL (low-frequency component in both the horizontal and

vertical directions), HL (high and low-frequency component in the

horizontal and vertical directions, respectively), LH (low and high-

85

frequency component in the horizontal and vertical directions, respectively),

and HH (high-frequency component in both the horizontal and vertical

directions).

Then, each sub region was decomposed again. From that, 16 sub

regions (two-level decomposition) are obtained. Then, 16 sub regions were

decomposed again and 64 sub regions (three-level decomposition) are

obtained. From the 64 sub regions, the mean and standard deviations of sub

region are measured and obtained as 128 (= 64 sub regions × 2 features)

features. The number of decomposition level was determined with which

maximum accuracy of PV recognition was obtained. Greater weight

values to the extracted features from LH region than those from other

regions. The optimal weight values were selected based on the maximum

authentication of accuracy with the training data of 2 000 images.

Then, the Euclidean distance between the extracted 128 feature values

and the enrolled one was calculated. Because the features have continuous

value, Hamming distance could not be used. By comparing the accuracy

of PV recognition in case of using Euclidean distance and cosine distance,

the Euclidean distance which showed high accuracy rate was

calculated. Whereas the PV code of 6,912 bits by LBP represented local

86

characteristics of PV such as detail shape of PV region, 128 feature values

by Wavelet transform did global characteristics such as the rough shape of

PV region.

3.14 SVM Classification

To combine the Hamming distance (HD) by LBP and the Euclidean

distance (ED) by Wavelet transform, a SVM (Support Vector Machine) was

used. In the past, the SVM was used to solve two class problems by

determining the optimal decision hyper plane. It is based on the concept of

structural risk minimization, since it measures the maximum distance to the

closest points of the training set. These measurements are known as support

vectors. For SVM training, half the images in the dataset were used. The

other half was used for testing. Two distances (HD and ED) were used as

the input values of the SVM. The output value of the SVM was

represented as a continuous value. A value that was close to 1

represented a genuine user and a value that was close to −1 represented an

impostor. The genuine means the user whose PV code and feature were

enrolled legally. The impostor is the user whose vein code and features

were not enrolled. From recognition performance based on the accuracy

and the prime was the output values of SVM, a genuine or impostor user

87

based on threshold was determined. The threshold limit was fixed with the

training data of 2000 images. Because the outputs of SVM is categorized

into two distributions such as genuine and imposter ones, the optimal

threshold based on conventional Bayesian rule was determined.

3.15 Experimental Results

In the first experiment, the recognition performance was tested.

First, measured. The d prime value means the classifying ability

between authentic and impostor distribution, defined by Equation (4)

d‘=μ1-μ2/√σ1^2 +σ2^2/2 (4)

Where μ1 and μ2 represent the means of authentic and imposter

distributions, respectively, and σ1 and σ2 represent the standard

deviations of authentic and imposter distributions. The greater the d′

value was, the more separable the two distributions and the accuracy

became higher. The accuracy and d prime of recognition are shown in

Table 3.1.

In case of ―only using HD by LBP (Local Feature)‖ and ―only

using ED by Wavelet transform (Global Feature).‖ The proposed method

requires half of data for the training of SVM, total 2000 images were used

for training and the other 2000 images were used for testing.

88

Although the shifting and matching scheme of PV code and features

were used, to pre-align the PV image based on the detected PV region and

minutia points such as bifurcation and ending points of PV lines is planned.

3.16 Summary

Table 3.1 The Accuracy Rate of PV Images

Methods No. of

PV

Accuracy

(%)

Processing

Time (mille

PV recognition with correlation

method using curvelet and Neural

2000

82%

13ms

PV recognition with correlation

method using line tracking algorithm

2000

85%

11ms

PV recognition with Correlation

Method only

2000

90%

9ms

Our Proposed Method (Correlation

with SVM classification)

2000

99%

6ms

A novel PV recognition algorithm was proposed. The proposed

algorithm is robust against irregular shading and saturation factors by using

the local and global features. As a result, the accuracy was 99% and the entire

processing time was 6 mins.

89

Figure 3.21 Graphical Representation of Accuracy

Performance of PV Images

The figure 3.21 shows graph for correlation method only recognition,

with line tracking algorithm and SVM based recognition. SVM based

recognition had reached more number of tested PV images for 2000 trained

PV images compared to correlated method. In future work, pre-aligning the

PV image based on the detected PV region and minutia points such as

bifurcation and ending points of PV lines is planned. To increase the

90

dataset including more different ages, genders and occupations is also

aimed.

Figure 3.22 Graphical Representation of Processing Time

Performance of PV Images

The figure 3.22 shows graph for curveletand neural networks based

recognition, line tracking algorithm and SVM based recognition.

Curveletand neural networks based recognition had reached more number

of processing time performance in mille seconds for 500 PV images

compared to line tracking algorithm and SVM based recognition.

91

CHAPTER 4

MULTI-VARIANT VOLUMETRIC MEASURE ON UPPER

EXTREMITY VP BASED PV RECOGNITION USING WAVELET

TRANSFORM

4.1 Introduction

The PV recognition has become most popular person authentication

approach where the recognition part is more complicated and has many

stages. Generally the human hand VP is used for authentication purpose

which is more secure and could not be malformed by others. The nature of

VPs and structural information supports the authentication process to be

performed in efficient manner.

The palm VP of any person is captured using the scanners of

ultrasonic type which is taken by passing the waves to the part of the hand.

The human vessels and veins are hard enough to reflect the ray comes from

the scanner. The reflected rays are captured to produce the PV and the

image acquired by this process becomes the only source to perform

authentication process.

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Figure 4.1 Palm VP of Hand

From the figure 4.1 the deep veins and superficial veins interlinked

each other frequently. The deep veins have arteries and have vanaeco

mitantes of the veins. The superficial veins have the following types

namely,

Digital Veins - The dorsal digital veins located and pass through

the sides of fingers and they are joined each other through

communicating branches. This makes three metacarpal veins and extends up

to the dorsal venus network.

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Cephalic Vein – it starts at the radical side of the venus network

and grows upward towards the radical part of the forearm.

Basilic Vein – starts at the venus network and grows through the

surface of forearm and inclined through the anterior surface.

Dorsal Metacarpal veins- The dorsal digital veins which surround the

fingers joins to form a three vein junction called metacarpal veins. Median

Vein- starts from the venous plexus and ends at the basilica vein.

Figure 4.2 Abstract VP of Human Hand

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The figure 4.2 shows the abstract view of palm VP and shows

the arteries, nerves and veins present in the palm VP of human.

From the above discussion, even though all the human has VP

w i t h the temporal features like shape, size vary between users and it will

be a distinct one for each other. In this approach such features to develop an

authentication framework to perform person authentication using PV images

are considered.

4.2 Methods Explored

Various approaches for personal authentication using PV images are

explored. PV Recognition Based on Three Local Invariant Feature

Extraction Algorithms Biometric Recognition [1], In contrast to minutiae

features; local invariant features extracted from infrared PV have scale

property, translationproperty and rotation invariance property. To

determine how they can be best used for PV system of recognition,

comparative study is made on this paper based on comprehensive of three

local invariant feature extraction algorithms: SIFT - Scale Invariant Feature

Transform, SURF - Speeded-Up Robust Features and ASIFT – Affine SIFT

for PV recognition. Initially, the preprocessing of image is done through

histogram equalization, then local features are extracted by three algorithms

95

and at last the result was obtained by comparing the distance of Euclidean.

Good performances on our own database and Poly multispectral palm print

database is obtained with the experiments.

PV Verification System Based on SIFT Matching [2], present in this

communication a new biometric system based on the use of hand veins

acquired by an infrared imager. The vein image is characterized by

particular patterns after the binarization and preprocessing stage. One of the

unique work in the proposed system is to use SIFT descriptors for the

process of verification. The developed method only makes it necessary for a

single image for the enrollment step allowing a very fast verification. The

results obtained after the experiment on a database containing images of 24

individuals acquired after two sessions show the efficiency of the proposed

method.

PV recognition by combining curvelet Transform and Gabor filter [3],

the Curvelet Transform is good at extracting the linear features from the

images of PV and the excels in extraction of orientation features by Gabor

Filter. On the basis of investigating the above two different coding schemes,

in this paper a score-level fusion scheme for palm print/vein verification is

proposed. The proposed method was applied on the HK PolyU Database and

96

an EER of 0.1023% was achieved, which outperforms using the Curvelet

Transform or Gabor Filter alone.

PV recognition by combining curvelet Transform and Gabor filter [4],

Biometrics research based on PV recognition has been developed rapidly in

recent years. PV extraction and matching for personal authentication [5],

propose a scheme of PV based personal authentication. The method captures

the infrared PV image through the capturing device then applies multilevel

filter. Then it extract the features and performs matching to improve the

quality of biometric authentication.

In PV recognition with Local Binary Patterns and Local

Derivative Patterns [6], a promising new approach based on local texture

patterns is proposed. In initial stage, histograms of multi-scale Local Binary

Patterns (LBPs) and operators are investigated in order to identify new

efficient descriptors for palm VPs. Higher-order novel local pattern

descriptor based on Local Derivative Pattern - LDP histograms are then

investigated for PV description. The extraction methods are compared with

both features and evaluated in the framework of verification and

identification tasks. Extensive experiments on CASIA database - CASIA

Multi-Spectral Palm print Image Database V1.0 finds the LDP and LBP

97

descriptors which are better adapted to PV texture. Tests results on the

CASIA datasets shows that the best adapted LDP descriptors consistently

outperform their LBP counterparts in both PV verification and identification.

Palm-Vein Image Recognition of Human Using Discrete

Enhancement [9], the palm-vein-based approach attempts to be

more effectively accommodating the potential deformations, revolving and

translational changes by encoding the orientation preserving features. The

method takes the infrared PV image as the input and identifies the junction

point using the hand geometry algorithm and pose invariant algorithm to

handle the shape and position of the image. Finally the method computes the

rank matrix using which biometric authentication is performed.

PV Recognition System Using Hybrid Principal Component

Analysis and Artificial Neural Network [10], focuses on PV recognition

system using Hybrid Principal Component Analysis (PCA) and Self

Organizing Map (SOM). The PCA-ANN experiments were considered

twice when inputs to ANN were unscaled (raw scale between 0 and 255)

and scaled (scale between 0 and 0.9). The systems performance was

evaluated on the basis of different image resolutions, different

training datasets, and recognition time and accuracy of recognition. The

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scaled PCA-ANN and unscaled PCA-ANN gave an optimal recognition

accuracies of between 55% and 98% and 56%-99% respectively at a

resolution of between 30*30 and 60*60 pixels level of cropping. Also

perform the further experiments in determining the error rates so that the

scalability of the algorithms to the task of controlling access will be

investigated. The FAR and FRR were between 2.5%-12.5% for unscaled and

2.5-15% for scaled and 2%-82% for Unscaled and 1%-81% for scaled at

0.0001 threshold accordingly. EER was in the level of 9.839% for unscaled

PCA-ANN at 49.53 range of pixels resolution and 12.53% for the scaled

PCA-ANN at 46.37 range of pixels resolution. This showed that EER was

achieved at lower pixels resolution (46.37) for scaled PCA-ANN than

the unscaled PCA-ANN (49.53) which revealed that overall system

accuracy would optimally be attained by scaled PCA-ANN than the unscaled

PCA-ANN.

All the above discussed approaches have the problem of false

acceptance rate and higher error rate conditions. To overcome the problem

of false acceptance rate, a multi variant volumetric measure based PV

authentication approach is proposed.

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4.3 Overview of Multi-Variant volumetric Approach

In this approach (Figure 4.3) the user has to keep his hand over the

sensor for certain time and the scanner captures the VP using ultrasonic

rays. The captured image is resized to a fixed size and applied with wavelet

transform. The transformed image is applied with edge detection process to

get the concrete VPs. The edge detected image is then split into number of

integral images and for each integral image generated a number of junction

points are identified with their co-ordinates. With the number of interest

points and their co-ordinates, the volumetric measure is computed.

Using all these details, trustworthy of the user for each class is computed

and verified.

100

Figure 4.3 Proposed System Architecture-I

The proposed PV recognition approach has various stages and each

of them is discussed here in detail.

4.4 Normalization

The captured input PV image may have various sizes due to

the placement of hands over the sensor. Sometimes the user may keep the

hands with concave manner which reduces the size of the image to be

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vary with the placement of hands in normal without any space between

finger. To overcome this issue normalization is performed (Figure 4.4).

The input image is resized to a fixed size where the training set has the

same. For example if the training set has the image size of 300 pixels

then the input image also will be converted to the same size.

Figure 4.4 Block Diagram of Normalization

4.5 Wavelet Transform on Input Image

The normalized image is applied with wavelet transform to boost the

low level intensity pixels. The input image may contain different contrast

102

pixels, and need to be enhanced to identify the VPs. To perform this

activity wavelet transform which is well proven method for signal

processing is used. The wavelet transform increases the signal values

or the pixel intensity which makes the pixels of the image to be visible

and helps the edge detection process to come up with more efficient results.

4.6 Canny Edge Detection

The transformed image is applied with the canny edge detection

process. The canny edge detection is an efficient edge detection approach

which is performed using the gray scale values of the pixels. First the input

image is smoothened using the gradient filter which removes the noise

present in the image. At the next step the method identifies the location or

the pixel where the gradient value deviates in more range. Upon

identifying the location where the gradient changes more, the neighboring

pixels are identified and rounded and preserve the pixel. At the fourth

stage, double Thrseholding is applied and edge tracking is performed to

produce the final edge detected image.

4.7 Integral Image Generation

The integral image is generated using the box type filters which are

the small set of images generated using box filters which splits images

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into many number of sub image set. The input image is selected and

number of sub images is created based on the parameters m and n. Here

m and n specifies the width and height of the integral image to be

generated. The value of m and n is a width and height multiplier of the

image. For example for a image with size 300×300, the value of m and n

will be 3×5 or 5×3 and so on.

4.8 Feature Extraction

The method extracts the gray features from the each integral image

generated and the gray scale values of the pixels which are greater than the

threshold will be identified and such pixel are selected for further

processing. The more gray scale values on gray pixels forms the edges of

the PV and such pixels are identified using which junction points can be

identified. For each integral image the extracted features are represented

in a PV matrix where each pixel selected for processing is represented as

1‘s and others are kept as 0‘s. The generated PV matrix will be used to

identify the junction points and their co-ordinates in the PV image.

4.9 Junction Point Identification

The junction points are identified using the PV matrix where each

index of the matrix with the value 1 is used. The neighbors region is divided

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0

0

0

0

1

1

0

1

0

as 8 quarters and for each 45 degrees there exist a pixel and its value is

available in the PV matrix. From the neighbors and their values, the

pixels with positive notations are identified. For each angle considered, the

presence of positive pixel in the consecutive direction is verified and the

changes of angle in the pixel values are identified. If the pixel is at the

center, and the first top pixel and first bottom pixel are identified.

If the line is a straight one then both the pixels has to be positive

otherwise that are considered as a junction. Further the direction of both

the lines and store the values of coordinates and their count are

identified.

0

1

0

0

1

0

0

1

0

A B

TheTableA,shows that there is no junction or intersection present in

the integral PV matrix.

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0

1

1

0

1

0

0

0

0

0

0

0

0

1

0

0

1

1

The Table B shows that there exist a up-right intersection or

junction present in the PV matrix.

C D

From the table C, there is a right-down junction present in the

PV matrix and in the table D there exist left down junction present.

0

0

0

0

1

0

1

1

0

E F

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0

1

0

0

1

0

1

0

0

0

1

0

0

1

0

0

0

1

The table E shows the right-up intersection present in the matrix

and F shows the down-right intersection from the PV matrix.

1

0

0

0

1

0

0

1

0

G H

The tables G and H show the presence of inclined right

down intersection and inclined down left intersection in the PV.

I J

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1

0

1

0

1

0

0

0

0

0

0

1

0

1

0

0

0

1

The table I and J show the presence of inclined left down and

inclined downright intersection in the PV matrix.

0

0

0

0

1

0

1

0

1

K L

The table K shows the triangular up intersection and the table L shows

the presence of triangular down intersection.

1

0

0

0

1

0

1

0

0

M N

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∫

The table M and N show the presence of triangular intersection from

the PV matrix. The presence of junction points are verified using all these

stage of identification and identified intersection and the co-ordinate are

stored in a separate matrix named junction point co-ordinate matrix.

Algorithm of Junction Point Identification

Step1 : Read integral image IE

Step2 : Convert the image into PV matrix PVM.

Step3 : Initialize angle index AI=0, junction point matrix JPM.

Step4 : Split PVM into number of 3*3 matrix

Step5 : Locate the center of the matrix

For each angle of 45 degrees

If ( , , ( × 45) == 1,1,0) then

AI=n×45

End

End

For each angle of degrees 45

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∫

( (

If ( , , ( × 45) == 1,1,0)&&

( × 45)! = && × 45) − 160)! =

JPM(i) = co-ordinate of Pi.

Junction point count JPC=JPC+1.

End.

Step 6: Stop.

Table 4.1 Displays the Values of Junction Point Matrix

1,17,16

1,29,48

1,72,98

2,15,12

2,34,56

2,87,23

…

…

…

The table 4.1 shows the junction point matrix generated by the

proposed method. Each index has three values where the first value

represents the number of integral image, because there exist N number of

integral image generated from the proposed method. The second index

represents the x- coordinate value and the third value shows the Y-

coordinate position of the junction point. Not all the repeated points will be

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added to the junction point matrix. It will be verified for the presence of the

junction point in the JPM. If there exit no such entry for the junction

point identified then it will be added to the junction point matrix.

4.10 Junctional Volume Computation

The Junctional volume is the measure which represents the number of

Junction points it has in any integral image. There may be N number of

junction points present in the integral image but vary with different integral

images. Also the density of the Junction points also depend on the person to

person also it will be vary with the region also. The method counts the

number of junction points present in each regional image ie integral image

and computes the number of gray scale values participated in the junction

point selection. Based on these feature values the Junctional volume

computation is calculated.

4.11 Algorithm of Junctional Volume Computation

Input : Junction Point Matrix JPM set,

Step1 : Initialize Junctional volume matrix JVM, Mean

Junctional Volume MJV, Junction point count JPC.

Step2 : for each integral image Imgi

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∫

∫

Identify set of junction points identified.

Junction point set

JPS = ∑ ( ) ∈ ( ).

Junction point count JPC= sizeof (JPS).

Count number of positive pixels from integral image Imgi.

PP = × ∑ ( )

Compute Junctional volume JV. JV =

× ×

JVM(i) = ∑ +

End

Step3 : Compute mean of Junctional volume

mjv = ∑ ( )

( )

Step4 : Stop.

4.12 Spacial Volume Computation

Spacial volume is the measure which represents the special

distribution of junction points and is computed based on number of

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junction point and the number of pixels supports identifying the junction

points. The number of coordinate‘s lies on the boundary is identified. Based

on identified boundary points, the volumes of space occupied by the

boundary points are computed. Computed special volume is used to

compute the cumulative weight to identify the closure of the PV image

submitted.

Algorithm

Input : PV matrix Pvm, Junction Point Matrix JVM

Output: Cumulative spatial weight.

Step1 : Identify the integral images at the boundary

Step2 : Generate the region of interest image ROI image.

Step3 : find the integral images belongs to the ROI image.

Step4 : for each PV matrix pvm

Identify the coordinates at the boundary.

Coordinate matrix

cm = ∑ ( , ) + ∫ ( , ) <> ( , max))

End

Step5 : Compute the volume occupied

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sp = ∫ (∑ ( ))

Step6 : Cumulative spatial weight

CSW =

Step7 : Stop.

4.13 Trustworthy Measure Computation:

The trustworthy measure is computed using the spatial volume and

Junctional volume. The calculated measure of trustworthy could be used to

identify the person identification. The trustworthy measure shows the

closeness of the submitted PV image with the available VP in the training

set.

Algorithm

Input : Spatial volume SV, Junctional volume JV.

Output: Trustworthy measure Tm.

Step1 : Initialize trustworthy measure tm= 0.

Step2 : Compute tm = SV×JV

Step3 : Stop.

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4.14 Summary

A multi variant volumetric measure to perform PV recognition is

proposed. The method normalizes the image by resizing the image and

applies wavelet transform to increase the signal levels. The

transformed image is used to generate number of integral image and for

each integral image the set of Junctional points and their coordinates are

identified. The identified features are presented as PV matrix and using them

the Junction volume and special volume to compute the trustworthy measure

of the PV given are computed. The method produces efficient results in the

false acceptance rate by reducing the rate. Moreover, it improves the

accuracy of PV identification and authentication. The method reduces the

overall time complexity which is higher in other approaches.

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CHAPTER 5

MULTI LEVEL DORSAL-DEEP VP BASED PV

AUTHENTICATION USING WAVELET TRANSFORM

5.1 Introduction

PV authentication has become more popular where the requirement of

person identification needs more secure procedures. The environment like

secure storage systems of banking or any organization needs access

control with more complicated and secure authentication process. The smart

card systems, finger prints, facial identification has more impact to actions

of malformed. The earlier methods like person recognition suffer with the

problem of malformed intrusion which could be performed by producing

fake finger prints or facial mask and so on. To overcome the issue present in

the earlier methods of person identification and person authentication, an

alternate solution emerged as the palm VP where the palm VP of each

human is distinct but has set of features as common.

How the palm VP could be used for person identification is,

the person has to keep his hands over a sensor for few seconds. The sensor

captures the palm VP using the ultrasonic scanner attached with the system.

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The scanner sends near ultrasonic rays over the hand which is reflected

by the human veins. The muscles of the human hand absorb the rays and

the veins reflect the rays. The reflected rays are captured and produced

as a black pixel and white pixel representing remaining regions. The

scanner returns a gray scale image which is the palm VP of the human hand

placed over the scanner.

Figure 5.1 Displays the Abstract VP

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The figure 5.1 shows the generic VP of human palm and it has

superfacial veins and deep veins, the palm VP can be classified into two

layers of veins namely superfacial extremity veins and deep veins.

The Dorsal deep veins are the arteries which forms the

vanaecomitantes. There will be two veins at the sides of arteries and they

are connected by means of short branches. These volar digital veins join

to the metacarpal veins and volar venous arches. Perforating branches of the

dorsal metacarpal veins receive from the volar metacarpal veins and end

in the radial veins and in the superficial veins on the dorsum of the wrist.

With this idea of the palm VPs the recognition process has to be

designed in efficient manner. There are many approaches has been designed

for PV recognition. The junction points, shapes of palm is heavily used. The

method which uses only the junction point counts the number of points

available in the PV image. This kind of approach has poor

detection accuracy because the number of points present in the image is

depend on the scanner quality and the way the user keeps his hands over

the scanner. Both of them affects the accuracy of the authentication

system and cannot be used. In case of shape feature based methods, the

118

hands and their shapes are considered by the components. Here the user

may place his hands in any angle which produces rotation in the image.

The method has to handle the rotational operation and has to transform the

image to particular angle. To perform this method has to find out the angle

of rotation the person did and then the rotation of captured image has to

be done. This is a most complicated approach and need more time to

perform authentication also produces poor results.

PV Recognition Based on Three Local Invariant Feature Extraction

Algorithms Biometric Recognition [1], In contrast to minutiae features;

local invariant features extracted from infrared PV have properties of scale,

translation property and rotation invariance property. To determine

how they can be best used for PV recognition system, here conducted a

comprehensive comparative study of three local invariant feature extraction

algorithms: SIFT - Scale Invariant Feature Transform, SURF - Speeded-Up

Robust Features and ASIFT – Affine SIFT for recognition of PV. First, the

images were preprocessed through equalization of histogram, then in total of

three algorithms were used to extract local features and the results were

obtained by comparing the distance of Euclidean. Good performances on

our own database and PolyU multispectral palm print database are

119

achieved by the experiments.

The earlier methods consider the top layer of the image where only

the dorsal veins present and they never consider about the deep veins which

are the most important factor affects the authentication process. The scanner

has to be efficient and well effective to capture the low level reflections to

produce the efficient PV image. Usually the scanner finds and picks the

reflected rays from the human veins and ignores the rays which are below

certain level. This makes the missing rays and could not produce the deep

VPs.

To perform more accurate recognition on palm VPs the scanner has to

be designed well enough to capture the low level signals and has to produce

black pixels in the image. Also the scanner has to produce the black pixels

with gray values according to the strength of the rays received from the

veins of palm.

This makes the recognition process to be performed in more efficient

manner. How it could be adapted and modified is using the wavelet

transform component with the scanner. Generally the scanner has the sensor

and transmitter and receiver. The ultrasonic waves passed through the hands

with certain strength by the transformer and the receiver receives the

120

reflected rays. A new design of scanner which sends the ultrasonic rays

with little more strength than general one and has a wavelet transformation

component at the behind the receiver is proposed. Whatever the signals or

rays received will be given to the wavelet transformation component and

it performs strengthening process which boosts the signal level and gives

back. The scanner generates the black pixel at the image based on the signal

level. Here the scanner has to keep two or more level of gray values where

certain range of signals or ray will be produced with high level gray

values and the next level rays will be produced with next level gray

values.

This modification in the scanner could help us in capturing the

reflections from the deep veins which makes the PV recognition as more

meaningful one. With this idea a novel Dorsal-Deep VP Based Vein-

Artery Measure Based PV authentication approach is proposed.

5.2 Overview of Dorsal-Deep VP Based Approach

Unlike other approaches the method considers both dorsal and deep

VPs for the recognition of PV images. The method reads the input image

and applies Gabor filter which removes the noise from the image.

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The noise removed image is applied with histogram equalization

technique which enhances the input image quality. The image which is

improved on its quality is applied with wavelet analysis which splits higher

and lower signals to produce two different images where the dorsal VP is in

the higher order image and the lower order image represent the deep VPs.

The two images are split into number of small images and their

features are extracted to identify the junction points. The extracted junction

points are stored in a dorsal junction matrix and deep junction matrix. Based

on generated two matrixes to compute the depth measure of dorsal vein and

deep vein. Using these two measures a cumulative depth is computed

based on which the person identification is performed.

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Figure 5.2 Proposed System Architecture-II

123

The figure 5.2 shows the general architecture of proposed Dorsal-

Deep VP based PV classification approach. All the functional

components are explained in detail in this section.

5.3 Noise Removal

To perform noise removal in the input image, the popular Gabor

filter is applied. The image presentation based on Gabor function constitutes

an excellent local and Multi scale decomposition in terms of logons that are

simultaneously (and optimally) localization in space and domain of

frequency. Gabor functions (Multi scale filtering schemes had frames

frequently) are often used in current models of image representation in the

visual cortex because they are a good approximation to the receptive fields

of cortical cells which are simple. Anyway, Gabor functions are not

orthogonal, as a consequence the mostly used Gabor expansion is

computationally expensive, having unusual dual basis functions. The

reconstruction needs the use among iterative algorithms, neural networks or

the inversion of large matrices.

A Gabor filter is a linear filter whose impulse response is defined by

a harmonic function multiplied by a Gaussian function. Because of the

property named multiplication-convolution, the term Convolution theorem

124

can also be said. The Gabor filter's Fourier transform impulse response is

the convolution of the Fourier transform of the harmonic function and the

Fourier transform of the Gaussian function.

5.4 Histogram Equalization

The next step is to enhance the quality of input PV image. The image

enhancement is performed using histogram equalization technique. 64 bit

histogram which will be used for further processing is generated. First the

set of possible intensity values between 0 and 256 are generated.

Computation takes place for each value of the set to compute set of pixels

comes with the grayscale value, the number of pixel with the same gray

scale values are computed. Then for each pixel, the round of operation to

equalize the values with the neighboring pixels based on computed

probability distribution is performed. This increases the image quality and

helps to identify the junction points in the next levels.

Algorithm of Histogram Equalization:

Input : Noise Removed image Img.

Output: Equalized Enhanced image Eimg.

Step1 : start

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Step2 : read input image Img.

Step3 : Ivset={0…256} (load possible intensity values).

Step4 : for each value in Ivset

Compute tp = total pixel intensity value Ivset(i) / total no of pixels.

End.

Step5 : for each pixel p in image Img

Perform transformation by rounding the Intensity values nearer. T(k) =

round(L-1) Σn=0-kpn

Compute probability distribution.

Pn – probability distribution

End

Step 6: stop.

5.5 Wavelet Analysis

The wavelet analysis is performed in the enhanced image to

separate the low energy pixels from higher energy pixels. The method

does not consider the gray values less than 100 and identifies the pixels

which are greater than 200 and which are between 100 and 200. The

126

wavelet analysis is performed to identify the low energy pixels and higher

energy pixel. The method generates two set of binary images and initialize

the pixels of the image as zero. For each pixel from the equalized

image the method identifies the pixel which is coming in the range of

gray values. The identified pixels and their index in the binary image is

triggered on and left the rest of all pixels in the off mode. Similarly the

same is performed by the method for the low level energy pixels and

generates deep vein image.

Algorithm of Wavelet Analysis:

Input : Enhanced Image Img

Output: Dosar vein image Domg, Deep Vein Image Dmg.

Step1 : Read input image

Initialize DT - Dosar Tolerance, DeT - Deep Tolerance.

Step2 : for each pixel Pi from Img energy of pixel needs to compute

Er = GrayValue(Pi(Img)) If Er ± DT then Domg(pi)=1.

Else if Er ± DeT then Dmg(pi)=1.

End

End.

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Step3 : stop.

5.6 Sub-Sampling Image Generation

The sub sampling image is a set of tiny images produced from the

original image given. In our approach, the deep vein image and the

dosarimage generated from the wavelet analysis is used to produce the sub-

sampling image. The method always has a fixed box size based on which

the input image is split into N number of images. The generated images are

used to produce the junction points present in the image.

Pseudo Code:

Input : Image IMG.

Output: Sub-sampled image set IIS.

Step1 : initialize box size M.

Step2 : while (M×M)<sizeof(IMG) Generate image

I = ∫ ( × ) IIS = ∑I(IIS)+I.

End.

Step3 : Stop.

5.7 Junction Point Computation

Generated subsample image set is used to compute the junction points

127

130

∫

present in each of the deep vein image and dosar vein images. There are

set of sub sampled image for each image and for each of the sub image from

the set given the presence of junction points are identified. For each pixel

which is in positive value, for the presence of minimum positive occurrence

in the eight neighbors are checked. There must be minimum of two positive

values to be present to conclude the presence of junction point. The

method maintains the junction point matrix where it stores the details of

junction point identified on each of the image. The method stores the

number of the junction point, and number of positive neighbor pixels are

found and so on.

Algorithm:

Input : Dosar Image Dimg, Deep vein image Dvimg.

Output: Dosar Junction matrix Djm, Deep junction matrix Dejm.

Step1 : Initialize Djm, Dejm.

Step2 : for each pixel Pi from Dimg

K= ∑( ( ) == 1),1,0)

If k>1 then

If Pi then

128

131

∫

Djm(i) = {Pi, K}

End

End.

Step3 : for each pixel Pj from Dvimg

L= ∑( ( ) == 1), 1,0)

If L>1 then

If Pj then

Dejm(i) = {Pi, K}

End

End.

Step4 : Stop.

5.8 Dorsal Depth Measure

The dorsal depth measure shows the cumulative density of junction

points present in the all the sub sampled regions. For each dorsal image,

junction point matrix has number of junction point has been identified and

computation takes to identify how many junctions has present for each

junction point and so on. Based on these values, the dorsal depth measure is

computed. The weight of junction points present in the super facial VPs is

129

132

∫

represented by dorsal depth.

Algorithm:

Input : Dorsal Junction Point matrix DJPM set DPMS.

Output: Dorsal depth measure DDM.

Step1 : for each junction point matrix Djpm from DPMS

Compute number of junction point

Njp = ∫ ∑ ( )! =

Compute junction density measure JD.

JD= ∑ ( )

DDM= ∑DDMi+JD

End

Step2 : Compute dorsal depth measure

DDM = ∑ ( )

( )

Step3 : Stop.

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133

5.9 Deep vein Depth Measure

The deep vein depth measure shows the cumulative density of

junction points present in the all the sub sampled regions of deep vein

images. The junction point matrix of each deep vein image has number of

junction point has been identified and for each junction point how many

junctions has present and so on. Based on these values, the deep vein depth

measure is computed. The weight of junction points present in the deep VPs

are represented by deep vein depth.

Algorithm:

Input : Dorsal depth measure Ddm, Deep vein depth measure Dvdm.

Output: Classification result Cr.

Step1 : From each class image from training set

Compute cumulative weight

TCw = TDdm*TDvdm.

Compute cumulative weight

CW = Ddm*Dvdm.

Compute distance between them

Ed = Euclidean(TcW,CW)

131

134

Add to distance set

Ds = ∑Edi(DS)+Ed.

End

Step2 : Compute average distance

Avd = ∑ ( )

( )

Step3 : if Avd< Threshold

Recognize as positive

Else

Recognize negative.

End.

Step4 : Stop.

5.10 PV Recognition

The PV recognition is performed based on computed deep vein depth

measure and dorsal vein depth measure. Using these two measures the

overall closure for any person based on the palm VP are computed. The

computed cumulative weight represents the closure of the PV

submitted with the trained set.

132

135

Algorithm:

Input : Dorsal depth measure Ddm, Deep vein depth measure Dvdm.

Output: Classification result Cr.

Step1 : From each class image from training set

Compute cumulative weight

TCw = TDdm*TDvdm.

Compute cumulative weight

CW = Ddm*Dvdm.

Compute distance between them

Ed = Euclidean(TcW,CW)

Add to distance set

Ds = ∑Edi(DS)+Ed.

End

Step2 : Compute average distance

Avd = ∑ ( )

( )

Step3 : if Avd< Threshold

Recognize as positive

133

136

Else

Recognize negative.

End.

Step4 : Stop.

5.11 Summary

A multi-level dorsal-deep VP based PV recognition approach is

proposed. The method removes the noise and performs histogram

equalization to enhance the image. The enhanced image is applied

with wavelet analysis and splits the higher order and lower order VP.

Generated two different images are split into sub sample images and

their junction points are identified. Identified junction point matrix is used

to compute the dorsal depth and deep vein depth measure to compute

the cumulative weight. Based on cumulative weight an average

distance measure is computed to identify the person base on some

threshold value. The proposed method has produced efficient results and

reduces the false ratio and time complexity.

134

137

CHAPTER 6

RESULTS AND DISCUSSION

A PV recognition approach has various strategic impacts in biometric

authentication systems and to improve the efficiency and to provide more

strategic solutions, different approaches at different levels are

proposed. This chapter discussed about the experimental analysis and results

produced by them. Two different approaches namely ―Multi-Variant

Volumetric Measure on Upper Extremity VP Based PV Recognition

Using Wavelet Transform‖ and ―Multi Level Dorsal-Deep VP Based

PV authentication Using Wavelet Transform‖ is proposed and results

produced by these approaches are discussed in detail in this chapter.

Table 6.1 Details of Data Set Being Used

Parameter

Value

Number of Classes 250

Number of samples per class 10

Total Number of samples 2500

Size of training set 70 percent

Size of testing set 30 percent

135

138

The table 6.1 shows the details of data set being used to evaluate the

performance of the proposed method. It specifies that 250 classes of

samples where each class has 10 numbers of images have used.

6.1 Multi-Variant Volumetric Measure on Upper Extremity VP

Based PV Recognition Using Wavelet Transform

In this method, the captured image is resized to a fixed size and

applied with wavelet transform. The transformed image is applied with

edge detection process to get the concrete VPs. The edge detected image is

then split into number of integral images and for each integral image

generated a number of junction points are identified with their co-ordinates.

With the number of interest points and their co-ordinates, the volumetric

measure is computed. Using all these details, finally the trustworthy of the

user for each class is computed and verified.

136

139

Figure 6.1 Snapshot of Input PV Image Selected

The figure 6.1 shows the snapshot of selected input image to perform

authentication and a right hand with inclined image which has to be

rotated to get the features of the palm VP are selected. It will be further

used in boundry marking process.

137

140

Figure 6.2 Snapshot of Boundary Marked

The figure 6.2 shows the boundary of the PV identified and marked.

From the figure 6.2, the second image named as Boundary image shows the

boundary points marked in red color lines and the corner points are marked

with green stars. The process of extraction will be carried out in the marked

region.

138

141

Figure 6.3 Rotated Snapshot of PV Image and the Region Marked to be

Extracted

The figure 6.3 shows the region marked with blue lines which is the

region of interest to be extracted from the PV image for processing. It

shows clearly that the portion has been marked and ready for feature

extraction. The region marked gets rotated for its next step.

139

140

Figure 6.4 Snapshot of Extracted Region of Interest

The figure 6.4 shows the snapshot of PV region being extracted from

the input image and it shows clearly that the portion marked in the previous

stage has been extracted for further processing. This extracted region had

some unwanted signals on it and it should be removed by noise removing

technique.

141

Figure 6.5 Snapshot of Noise Removed Image

The figure 6.5 shows the snapshot of image which is removed from

noise present in the ROI image. It shows that the image has been removed

from the noise present in the image. Eventhough noise get removed, it still

required to remove background for further proceedings.

142

Figure 6.6 Snapshot After Background Removal

The figure 6.6 shows the Region of interest image after

performing the background subtraction process and the background

subtraction has been performed by binary imaging technique. After the

background removal it can not be used directly for skeleton identification

process, to do so the background removed image should be normalized.

143

Figure 6.7 Snapshot of Normalized PV Image

The figure 6.7 shows the snapshot of normalized PV image which

will be used to perform skeleton identification process. The

normalized image has veins with larger dimensions and so that the veins

skeleton has to be identified to perform junction point identification and to

perform other computation process.

144

Figure 6.8 Snapshot of Skeleton Identified Image

The figure 6.8 shows the skeleton generated PV image. It

shows clearly that the proposed method has produced efficient skeleton

of PV image. It shows the collection of images such as input image,

boundary image, vein region image, region extracted image, filtered image,

background removed image, extracted image and skeleton image.

145

Figure 6.9 Snapshots of Identified Junction Points in the Image

The Figure 6.9 shows the details of junction points has been identified

from the given input image. It shows that there are number of points has

been identified which will be used to compute the volumetric measure

between the points. The result of PV matching or not matching is obtained

by the values of volumetric measure between the points.

146

Figure 6.10 Snapshot of PV Image Matched

The figure 6.10 shows the PV image gets matched with the proposed

method. The skeleton image is used to match the PV image. After matching

the PV image, the step by step result of proposed method can be drawn.

147

Figure 6.11 Snapshot of Step by Step Result of Proposed Method

The figure 6.11 shows the snapshot of result produced by each step at

one shot. It shows clearly that the proposed method has produced efficient

results. From the above discussions presented, the proposed method has

been presented with various stages of results. The method has identified

various junction points from the input image and has computed volumetric

148

measure between all the junction points. By calculating the volumetric

measure between all the measures a cumulative special volume has been

computed. Computed special volume has been used to compute trustworthy

measure of the given input image to perform authentication of the PV

image.

149

Graph 6.1 Comparison of PV authentication Accuracy

The graph 6.1 shows the comparison of authentication

accuracy produced by different methods. It shows clearly that the

proposed method has higher accuracy than other methods. The proposed

(Multi-Varient) method reaches 96% of accuracy where LBP/LDP, SIFT,

PCA/ANN had lesser accuracy than proposed method.

150

Graph 6.2 Comparison of False Positive Ratio of Different Methods.

The graph 6.2 shows the comparison of false result produced

by different methods. It shows clearly that the proposed method has

produced less false results than other methods. The proposed (Multi-

Varient) method only produces 4% of the false rate which is lesser than the

other methods.

151

Graph 6.3 Comparison of Time Complexity of Different Methods.

The graph 6.3 shows the comparison of time complexity produced by

different methods on varying number of samples and classes. It proves

clearly that the proposed (Multi-Varient) method has produced less time

complexity than other methods at different number of classes.

152

Table 6.2 Comparison of Resilience, Rotation and Noise

Method

Translation

Rotation

Noise

Local Binary No Yes No

SIFT No Yes No

PCA No No Yes

Proposed Yes Yes Yes

The table 6.2 shows the comparison of resilience, rotation and noise

factors considered in different approaches. Unlike other approaches, the

proposed method has considered all the factors in performing PV

recognition.

6.2 Multi-Level Dorsal-Deep VP Based PV authentication Using

Wavelet Transform

The method reads the input image and applies Gabor filter which

removes the noise from the image. The noise removed image is applied with

histogram equalization technique which enhances the input image quality.

The quality improved image is applied with wavelet analysis which splits

higher and lower signals to produce two different images where the dorsal

VP is in the higher order image and the lower order image represent the

deep VPs. The two images are divided into number of small images and

153

their features are extracted to identify the junction points. The extracted

junction points are stored in a dorsal junction matrix and deep

junction matrix. Based on generated two matrixes the dorsal vein depth

measure and deep vein depth measure are computed. Using these two

measures a cumulative depth is computed based on which the person

identification is performed.

Figure 6.12 Snapshot of Input Image Selected for PV Recognition

154

The figure 6.12 shows the snapshot of the PV image selected

to perform PV recognition and the image selected is displayed in the first

axes component of the mat lab GUI component. This input image can be

further extracted for next stage of process.

Figure 6.13 Snapshot of Region Extracted

The figure 6.13 shows the snapshot of region of interest being

extracted from the input image which will be used to perform histogram

155

equalization and identifying junction point identification.The extracted

image can be further processed for equalization in terms of histogram to

produce histogram equalized ROI image.

Figure 6.14 Snapshot of Histogram Equalized ROI Image

The figure 6.14 shows the snapshot of histogram equalized image

which is performed on the region of interest image. This histogram

equalized ROI image can be obtained by the extracted region of input

156

image. This obtained image can be further taken for background subtraction

in next process.

Figure 6.15 Snapshot of Background Subtraction

The figure 6.15 shows the snapshot of background subtraction image

obtained from the histogram equalized image. It shows that the white

region is the feature considered to perform edge detection and junction point

157

identification. After the process of background subtraction, the image can

be proceeded for normalizing process.

Figure 6.16 Snapshot of Normalized Image

The figure 6.16 shows the snapshot of normalized image obtained

from background subtracted PV image which will be used to perform edge

detection and junction point identification.

158

Figure 6.17 Snapshot of Junction Point Identified Image

The figure 6.17 shows that the edge detected and junction point image

obtained from the normalized image and also displays the result of

the complete approach. This image is a collection of input image, region

marked image, rotated image, region of interest image, histogram equalized

image, background removed image, binarized image, junction point image

and identified palm vein image.

159

Figure 6.18 Snapshot of Identified PV Image

The figure 6.18 shows the snapshot of PV image being identified by

the proposed multi-layer approach. After the process of junction point

identification, the PV image gets matched with the existing database. This

process of matching PV image is done in lesser time and with greater

accuracy achived when comparing with existing techniques.

160

Graph 6.4 Comparison of PV authentication Accuracy

The graph6.4 shows the comparison of authentication accuracy

produced by different methods. Multi-Layered is nothing but a proposed

method and is compared with other existing methods such as LBP/LDP,

SIFT, PCA/ANN. It shows clearly that the proposed method has higher

accuracy than other methods.

161

Graph 6.5 Comparison of False Positive Ratio of Different Methods.

The graph 6.5 shows the comparison of false result produced

by various methods. It shows clearly that the proposed method has

produced less false results than other methods. The proposed Multi-Layered

method is well achived in lesser false ratio had only value of 4%.

162

Graph 6.6 Comparison of Time Complexity of Different Methods.

The graph 6.6 shows the comparison of time complexity produced by

different methods on varying number of samples and classes. It shows

clearly that the proposed method has produced less time complexity than

other methods at different number of classes. Here Multi-Layered proposed

method is compared with PCA/ANN and SIFT methods. The proposed

method shows clearly that it takes only lesser time for any number of

samples.

163

6.3 Comparative Analysis

Two approaches for building of PV authentication technology

are proposed and each has been tested with different number of classes and

samples and produced efficient results in all the factors of quality of PV

recognition and authentication. Here the proposed two methods are Multi-

Varient and Multi-Layered. It is clear that both the proposed method have

more accuracy than other existing methods such as PCA/ANN, SIFT,

LBP/LDP.

Graph 6.7 Comparison of PV authentication Accuracy

164

The graph 6.7 shows the comparison of PV authentication accuracy

produced by different methods and it shows that the Multi-Layered and

Multi-Varient methods have produced efficient accuracy than the other

methods at different number of classes and samples.

165

CHAPTER 7

CONCLUSION AND FUTURE WORK

The appropriate image segmentation technique on VP is applied.

Two different approaches namely ―Multi-Variant Volumetric Measure on

Upper Extremity VP Based PV Recognition Using Wavelet Transform‖

and ―Multi Level Dorsal-Deep VP Based PV authentication Using

Wavelet Transform‖ is proposed.

The proposed multi variant volumetric measure method normalizes

the image by resizing the image and applies wavelet transform to increase

the signal levels. The transformed image is used to generate number of

integral image and for each integral image should identify the set of

Junctional points and their coordinates. The identified features are presented

as PV matrix and it compute the Junctional volume and special volume to

compute the trustworthy measure of the PV given. The method produces

efficient results in the false acceptance rate by minimizing the rate. Also it

improves the accuracy of PV identification and authentication. The method

reduces the overall time complexity which is higher in other approaches.

The proposed multi-level dorsal-deep VP based PV recognition

approach removes the noise and performs histogram equalization to enhance

166

the image. The enhanced image is applied with wavelet analysis and

splits the higher order and lower order VP. Generated two different

images are split into sub sample images and their junction points are

identified. Identified junction point matrix is used to compute the dorsal

depthness and deep vein depthness measure to compute the cumulative

weight. Based on cumulative weight an average distance measure is

computed to identify the person base on some threshold value. The proposed

method has produced efficient results and reduces the false ratio and time

complexity.

The method reads the input image and applies Gabor filter which

removes the noise from the image. The noise removed image is applied with

histogram equalization technique which enhances the input image quality.

The quality improved image is applied with wavelet analysis which splits

higher and lower signals to produce two different images where the dorsal

VP is in the higher order image and the lower order image represent the

deep VPs. The two images are split into number of small images and their

features are extracted to identify the junction points. The extracted junction

points are stored in a dorsal junction matrix and deep junction matrix. Based

on generated two matrixes, the dorsal vein depthness measure and deep vein

167

depthness measure are computed. Using these two measures a cumulative

depthness is computed based on which the person identification

is performed.

Experimental results shows that the snapshot of region of interest

being extracted from the input image which will be used to perform

histogram equalization and identifying junction point identification.

Comparison of authentication accuracy produced by

different methods, experimental results show clearly that the proposed

method has 96% accuracy which is higher than other methods.

Comparison of false result produced by different methods, the

proposed method has 4% false which is lesser comparatively in the result of

other methods.

Time complexity produced by different methods on varying number

of samples and classes are compared and the result shows clearly that the

proposed method has produced less time complexity than other methods at

different number of classes. Experimental results show that the comparison

of resilience, rotation and noise factors considered in different approaches.

Unlike other approaches, the proposed method has considered all the factors

in performing PV recognition.

168

In the future work, pre-align up the PV image footed on the

discovered PV section and minutia ends can be identified. The same

approach can be used for combined multimodal PVs and face biometric

verification system which enhances the quality of biometrtic authentication

by extracting PV and facial features. The method combines both PV and

facial features to perform biometric authentication using similar

methodology. Bifurcation points and ending points are similar to finger

prints, these feature points are used as a geometric representation of the

shape of VPs. These geometric representation can additionaly be used for

improving security.

169

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LIST OF PUBLICATIONS

INTERNATIONAL JOURNALS

1. ―Image Pre-Processing Methods for Personal Identification‖,

Journal of computer Applications, Vol. 1, No. 4, pp.19–24, October–

December 2008.

2. “Hand Palm Vein Authentication by Using Junction Points with

Correlation Method”, International Journal Of Computational Engineering

Research, Vol. 03, Issue. 1, pp. 67-73, January 2013.

CONFERENCES

1. ―Efficient Palm print and Palm Vein Based Person Recognition

Using Junction Point with Correlation Method -An Illustration‖,

International Conference On Computer Science And Engineering, WASET

(BANGKOK, THAILAND), 25th-26th December 2011.

2. ―Palm Vein Authentication Using Wavelet Transform‖,

International Conference on Innovative Computing And Information

Processing, Mahendra Engineering College (Mallasamudram), 29th to31st

March 2012.

3. ―Multi-Variant Volumetric Measure on Upper Extremity Vein

Pattern‖, International Conference on Sustainable Approaches for Green

Computing ,Economy and Environment-SAGCEE-13, V.M.K.V Engineering

College, 09th to 11th December 2013.

190