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CHAPTER 3

PROPOSED PALMPRINT RECOGNITION SYSTEM

This chapter describes the algorithm used for personal

identification based on features extracted from the palmprint. First Local

Gabor XOR (LGXP) features are extracted from the palmprint using Gabor

filter with single orientation. The algorithm is then modified, where features

are extracted with different orientations of the Gabor filter called the Multiple

Orientation LGXP (MOLGXP) features. Next PCA feature is extracted and

the minimum matching score from the individual matchers of MOLGXP and

PCA are fused using sum rule. The performance of the proposed algorithms is

tested on the PolyU database provided by the Hong Kong Polytechnic

University. The block diagram of the proposed palmprint recognition system

is shown in Figure 3.1. The different blocks are explained in the subsections.

Figure 3.1 Block diagram of the proposed Palmprint Recognition system

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3.1 PREPROCESSING

The first step in palmprint based identification system is

preprocessing. During image capture the position, direction and the degree of

stretching of the palm may vary. Hence the palmprint from the same palms

may be subjected to slight rotation and translation. Thus the aim of this step is

to align different palmprint and extract the central palm area for feature

extraction and to eliminate variations caused by rotation and translation

(Zhang & Wang 2003). In the PolyU database, the positioning of the hand on

the scanner bed is guided by the presence of pegs and hence the acquired

palmprint is invariant to translation and rotation (Zhang 2004). Thus it is

sufficient to define a coordinate system for the extraction of the central palm

areas. Before extracting the desired palm area, each palmprint image in the

database is filtered using Median filter. The median filter is useful for

reducing speckle noise, salt and pepper noise. The median value is actually

one of the pixel values in the neighbourhood and hence no new pixel values

are created. This property of the median filter is particularly useful in

preserving the edges and hence serves to enhance palmprint images. The

palmprint image from the PolyU database and filtered images are shown in

Figure3.2.

Figure 3.2 a) Original images from PolyU database b) Filtered images

(a)

(b)

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After enhancing the palmprint image, the desired area called the

Region of Interest (ROI) is to be obtained. The ROI extraction must be done

carefully to avoid interclass variations. The valley between the fingers being

stable is used to establish a coordinate system from which the ROI is

extracted. The reference points are determined and the line passing through

the reference points forms the Y-axis. The horizontal line perpendicular to

Y-axis represented by the black line is the X-axis. The point of intersection of

the X-axis and Y-axis is the midpoint of the reference points A and B. After

reference points are located on rotated palm images, the next step is to extract

the central palm area, which is the square region shown in Figure 3.3.

Figure 3.3 Palmprint ROI extraction

Let the reference point A be denoted by ( , ) and point B

by ( , ). Let ( , ) be the midpoint of the reference points A and B. It

is given by

= = (3.1)

= (3.2)

The extracted square palm area has a length of pixels along the

horizontal and vertical direction. The perpendicular distance between the Y

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axis and the upper vertical side of the rectangular region is d pixels. Let

111 , yxS denote the coordinates of the upper left corner of the square region,

and similarly, 222 , yxS denote the coordinates of the lower left corner of the

square region,

= + (3.3)

= + /2 (3.4)

= + (3.5)

= /2 (3.6)

Next the right upper corner and the lower right corner coordinates

may be determined as follows:

= + (3.7)

= (3.8)

= + (3.9)

= (3.10)

Thus the coordinates of the ROI has been determined and the

desired central palm area is extracted. The original palm image and the ROI is

shown in Figure 3.4. The size of the palmprint image available in the PolyU

database is 384×284 and the extracted ROI is of size 120×120.

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Figure 3.4 Central Palm areas Extraction on PolyU database images a) Original images b) Extracted ROI

3.2 FEATURE EXTRACTION

Feature extraction plays an important role in image identification

and verification. A palmprint which may be defined as the skin patterns of a

palm consists of the physical characteristics such as lines, points and texture.

Features like principal lines, wrinkles and texture can be extracted from low

resolution images but delta point and minutiae features can be extracted from

high resolution images only. Palmprint capturing devices with high resolution

are costly and these being a disadvantage, in the proposed system low

resolution images are used. Many algorithms have been developed by

researchers to extract the principal lines but such systems do not provide high

accuracy because different individuals may possess similar line features

(Zhang et al 2003). Also extracting the wrinkles exactly is a difficult task and

hence 2D Gabor filtering is used to extract the texture features from low

resolution palmprint images.

(a)

(b)

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3.3 TEXTURE FEATURE

Texture is one of the important attribute that has been used by the

human visual system in identification of objects. Texture has been used in

many image processing and computer vision applications like segmentation,

classification and shape from texture. The texture patterns can be easily

identified by the humans but it is very difficult to define exactly. No specific

definition is found in the literature but different definitions are provided based

on applications or visual perception. Clark et al (1987) has defined texture as

“a spatial arrangement of local (gray-level) intensity attributes which are

correlated in some way within areas of the visual scene corresponding to

surface regions. According to Tamura et al (1978) it is defined as a repetitive

pattern in which elements or primitives are arranged according to a placement

rule. This visual repetitive pattern makes it easy for human visual system to

identify the texture patterns

Various approaches have been used by researchers for texture

analysis but psychophysiology studies show that the brain performs

multichannel frequency and orientation analysis of the visual image that is

formed on the retina. Campbell & Robson (1968) based on experimental

studies, showed that that the human visual system decomposes an image into

filtered images of different frequencies and orientation. Thus researchers have

been motivated to use multichannel filtering for texture analysis.

3.3.1 Gabor Filter

A Gabor filter has been widely used by researchers for the texture

analysis (Vyas & Rege 2006, Clausi & Jernigan 2000), face recognition

(Sharif et al 2011, Jin & Ruan 2009), iris recognition (Avila & Reill 2005,

Tsai et al 2009) and in fingerprint recognition (Lee & Wang 1999, Yang et al

2003) systems. The one dimensional Gabor filter was first introduced by

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Dennis Gabor (1946) and later it was extended for two dimensional signals by

Daugman (1980). Gabor filters provide advantages like 1) It can capture the

local information governed by the uncertainty principle. 2) It provides

robustness against varying brightness and contrast images. 3) It can be used to

model the receptive fields of the mammalian simple cells in the primary

visual cortex. According to Daugman (1993) Circular 2D Gabor filter can be

effectively used to extract texture information from images and is represented as

( , , , , ) = {2 ( + )} (3.11)

where = 1 ,u is the frequency of the sinusoidal wave, is the orientation

of the function and is the standard deviation of the Gaussian envelope. Such

Gabor filters have been widely used in various applications like fingerprint recognition, face recognition, and texture analysis.

The Gabor filter ( , , , , ) forms the complex valued function.

Decomposing ( , , , , ) into real and imaginary parts gives

( , , , , ) = ( , , , , ) + ( , , , , ) (3.12)

where ( , , , , ) and ( , , , , ) represent the real and imaginary

parts of the Gabor filter. In order to provide more robustness to brightness

variations, a zero mean Gabor filter is necessary. The mean value of the

imaginary part of the Gabor filter is automatically zero because of the odd

symmetry of the sine function but the mean of the real part of the filter is not

zero because of the even symmetry of the cosine function. A zero mean Gabor filter is obtained using the formula given below

( , , , , ) = ( , , , , )( , , , , )

( ) (3.13)

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where (2 + 1) represents the size of the filter.

The filtered image provides two types of information which can be

used separately or may be combined to provide the feature. They include

magnitude ( , ) and phase (x, y) which are given by the equation as

mentioned below (Kong et al 2006).

( , ) = ( , ) × ( , ) (3.14)

(x, y) = tan ( , ( , )( , ) ( , )

(3.15)

where “*” represents the convolution operation, “____”represents complex

conjugate and ( , ) the extracted palmprint image.

3.3.2 Local Gabor Exclusive OR Patterns (LGXP)

To determine the LGXP features, the phase value as given by

above equation is computed for each pixel of the filtered image. Next the

image is divided into 3×3 sub images and phase values are quantized based

on the range of the phase value. Now for each sub image the quantized phase

of the central pixel is compared with each of its neighbouring pixels and XOR

operation is applied. If the central pixel and neighbouring pixel are different,

then the neighbouring pixel is replaced by a binary 1 and if they are same

then the neighbouring pixel is replaced by a binary value 0. Finally the

resulting binary labels are concatenated together as the Local XOR Pattern

(LXP) of the central pixel. The steps involved in determining the LGXP

pattern is explained below.

As a first step the phase values in a sub image of size 3×3 are

quantized and coded based on the following rule

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( , ) = 0 0 ( , ) < (3.16)

( , ) = 1 ( , ) < (3.17)

( , ) = 2 ( , ) < (3.18)

( , ) = 3 ( , ) < 2 (3.19)

Next, the pattern of LGXP in binary and decimal form is defined as follows

( ) = , , … … … . (3.20)

= [ 2 . ] (3.21)

where denotes the central pixel position in the Gabor phase map with S

being the size of neighbourhood and ( = 1,2, … … . ) denotes the

pattern calculated between and its neighbour which is computed as

follows

= ( ) q Z , j = 1,2 … … S (3.22)

Where )•(q denotes the coded phase value of the pixel which is

equal to 0,1,2 and 3 , denotes the LXP operator, which is based on XOR

operator, as defined in equation given below

( ) q Z = 01

( ) (3.23)

The pattern map described above for a 3×3 sub image is calculated

for the filtered image as

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= [ , … … … ] (3.24)

where i=1, 2…..n denotes the number of sub images in the filtered image The

encoding process is shown in Figure 3.5.

Figure 3.5 Encoding method of LGXP

3.4 PRINCIPAL COMPONENT ANALYSIS (PCA)

The Principal Component Analysis (PCA) also known as the

Karhunen-Loeve transform is a classical statistical technique used in the

biometric systems like face recognition (Chan et al 2010, Turk & Pentland

1991), iris recognition (Cui et al 2004, Patil et al 2012), and character

recognition (Mane & Ragha 2009, Zuo et al 2002). The main aim of PCA is

to reduce the dimensions of the data so the features extracted could be

represented in reduced dimensional space. In PCA the data is projected into

an orthogonal subspace so that a number of correlated components can be

transformed into a smaller number of uncorrelated components. The first

principal component captures the variance of the data in a particular direction

and other principal components capture the remaining variability.

3.4.1 PCA Based Feature Extraction

In this section the feature extraction from the preprocessed image is

carried out using principal component analysis. Each preprocessed palmprint

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image of size N×N is represented as 1× N2 dimensional vector where each

row of pixels is concatenated so as to form a one dimensional vector. Let the

training samples be represented as ( , … ).where K is the total number

of training samples. The mean vector of the training samples and the

deviations from the mean vector are computed using the following relations

= (3.25)

= (3.26)

The Covariance matrix is next computed using the relation (3.27)

= ( )( ) = (3.27)

Where the matrix = { , … … . } .Next the Eigen vectors

of C is computed and m largest Eigen values are selected

= (3.28)

The Eigen vector corresponding to m largest Eigen values is

normalized and is given by the following relation

= (3.29)

The set of the Eigen vectors is composed of optimal linear

transformation matrix . The preprocessed palmprint image is then

transformed into the feature space by the following relation

= ( ) (3.30)

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3.5 RECOGNITION PROCESS

In this phase, a test image of the palmprint is taken and its feature

vector is computed by applying the steps described above. Then, the matching

score is calculated by comparing the feature vectors of the test palm image

with the feature vectors of the palm images available in the database using an

appropriate distance metric. Here, Euclidean distance measure is used to

compute the matching score. The following steps are involved in the

matching phase. The distance can be calculated by using the following

equation

= 1/ ( ) (3.31)

where = ( , , … ) = ( , , … )) represents the feature

vector of the test image and feature vector of the image in the database. After

the distance is computed the query palmprint image can be recognized by

using the thresholding technique which is described in the following pseudo

code.

Thresh t; // t is the average distance value

Ifmin(dist)< ThreshThen

Assign ismatch is True;

Else

Assign ismatch is False;

End if

The ismatch term is true then the image is recognized, otherwise it

is not recognized. The Thresh value is based on the matching scores

generated when the test sample is compared with the database sample.

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3.6 EXPERIMENTAL RESULTS

Three experiments were conducted and the identification results are

compared. Testing hand image sources are taken from Hong Kong

polytechnic university (PolyU) palmprint database. The PolyU palmprint

database contains 7752 grayscale images corresponding to 386 different

palms in BMP image format. Around twenty samples from each of these

palms were collected in two sessions where around 10 samples were captured

in the first and the second sessions, respectively. The palmprint images in the

database are labeled as "PolyU_xxx_L_NN.bmp", where the "xxx" is the

unique palm identifier (range from 001 to 386), "L" is the index of the first or

the second session ('F' indicates the first session while 'S' indicates the second

session) and "NN" is the index of each palm (range from 1 to 10).

Experiment Results for LGXP Feature: In this phase the LGXP

feature is computed with the parameters frequency =0.90, the space

constant = = =5.5 and orientation = 30 . The selection of the

parameters is based on the results provided by Kumar &Zhang (2005) and

Zhang et al (2003). A total of twelve samples are taken for each person from

the hand images captured during the first and second sessions in the PolyU

database. Out of these four samples are used during the training phase and

remaining samples are used in the testing phase. The LGXP parameters are

computed for the training samples and stored in the database. The total

number of training samples is 600 where four samples are trained for 150

persons. During the testing phase, for each test sample LGXP feature is

computed and this is compared with the LGXP feature templates stored in the

database using the Euclidean distance measure. If this distance measure is less

than the threshold, then test image is considered to be genuine, otherwise it is

an impostor. The threshold values are varied and for each of this value the

False acceptance Rate(FAR),False Rejection rate(FRR) and Genuine

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Acceptance rate(GAR) are calculated using the equations (2.1),(2.2),(2.3).The

outputs obtained for the LGXP feature are shown in Figure3.6.

(a) (b)

(c) (d)

(e) (f)

Figure 3.6 (a) Original PolyU palmprint image (b) Cropped image (c) Real part (d) Imaginary part (e) Magnitude part (f) Phase part at = 300

The Table 3.1 shows the experimental values obtained. The

threshold value is varied in steps of 0.1 from 0 to 1. The matching score

generated from the matcher is a genuine score if both the test sample and the

database template belong to the same person, otherwise it is an impostor

score. The FAR and FRR values are computed at each threshold value based

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on the number of genuine and impostor scores generated. From the Table 3.1,

it is observed that as the threshold value is increased the number of genuine

person rejected decreases whereas the number of impostors accepted by the

system increases. Hence it is observed that the false rejection rate decreases

and false acceptance rate increases as the threshold is increased.

Table 3.1 Error rates and Recognition rate of LGXP based Palmprint Recognition system

Threshold value FRR% FAR% GAR%

0 100 0 00.20 100 0 00.30 50 0 50.000.4 20 0 80.00

0.47 7.12 0 92.880.48 5.86 0.0065 94.140.49 4.62 0.0092 95.380.50 3.16 0.0380 96.840.51 2.06 0.2000 97.940.52 1.04 1.2200 98.960.53 0.33 2.1300 99.670.54 0.06 3.2800 100.000.55 0 4.6900 100.000.6 0 20.000 100.000.7 0 100.00 100.00

Also it is observed that for lower values of threshold, the rejection

rate is very high and system cannot be useful as a suitable recognition system.

For threshold values between 0.47 and 0.55, the False Rejection Rate and

False Acceptance Rate are in a tolerable range. A plot of FRR and FAR

against threshold between 0 and 0.8 is shown in Figure 3.7(a) and

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Figure 3.7(b) shows the plot of FRR and FAR against threshold values

between 0.47 and 0.55.

(a)

(b)

Figure 3.7 Plot of FRR%, FAR% against threshold of LGXP based Palmprint Recognition system (a) For threshold values between 0 and 0.8 (b) For threshold values between 0.47 and 0.8

Experiment Results for MOLGXP Feature: In this phase the

LGXP features are obtained for six different orientations namely

= 30 , 60 , 90 , 120 , 150 180 . The features thus obtained for

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various values of are concatenated to form the total feature vector. The real

and imaginary parts obtained for different orientations are shown in Figure

3.8 and the amplitude and phase part in Figure 3.9.

Figure 3.8 (a) Real and (b) imaginary parts for different orientations

(a)

(b)

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Figure 3.9 (a) Amplitude and (b) Phase for different orientations

The values of FAR, FRR and GAR are shown in Table 3.2 and plot

of FRR and FAR against threshold is shown in Figure 3.10.

(a)

(b)

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Table 3.2 Error rates and Recognition rate of MOLGXP based Palmprint Recognition system

Threshold value

FRR% FAR% GAR%

0.45 4.27 0 95.73

0.46 3.50 0.00022 96.50

0.47 2.62 0.00125 97.38

0.48 1.65 0.0090 98.35

0.49 1.14 0.01400 98.86

0.50 0.67 0.0800 99.33

0.51 0.17 0.1900 99.83

0.52 0.04 0.4020 99.96

0.53 0 0.7190 100.00

Figure 3.10 Plot of FRR%, FAR% against threshold of MOLGXP based Palmprint Recognition system

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Experiment Results for MOLGXP+PCA Feature: In this phase

the Gabor filter with six orientations are used and MOLGXP features are

extracted. In addition to the MOLGXP feature, the PCA features are also

extracted. Euclidean distance is used to match the PCA features. For a given

test image the two features are extracted and minimum matching scores are

determined for both of the features. The matching scores are then combined

using the sum rule. Let represent the matching score from the MOLGXP

matcher and from the PCA matcher. The combined average score using

the sum rule (Ross 2006) is given by

= ( + ) (3.32)

The values of FAR, FRR and GAR are shown in Table 3.3 and plot

of FRR and FAR against threshold is shown in Figure 3.11.

Table 3.3 Error rates and Recognition rate of MOLGXP+ PCA based Palmprint Recognition system

Threshold value

FRR% FAR% GAR%

0.45 2.83 0 97.17

0.46 1.75 0 98.25

0.47 1.02 0.00012 98.98

0.48 0.62 0.00800 99.38

0.49 0.42 0.04300 99.58

0.50 0.29 0.11400 99.71

0.51 0.12 0.13800 99.88

0.52 0.02 0.25000 99.98

0.53 0 0.35800 100.00

0.54 0 0.45600 100.00

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Figure 3.11 Plot of FRR%, FAR% against threshold of MOLGXP+ PCA based Palmprint Recognition system

The Figure 3.12 shows the Error Trade off Curves, as a plot of the

False Rejection Rate against False Acceptance Rate obtained at different

threshold values for LGXP, MOLGXP and MOLGXP+ PCA feature

extraction based Palmprint Recognition system as explained above. This

graph is useful in comparing the performance of different biometric systems.

From the graph it is observed that the error rates are more for LGXP feature

in comparison with the error rates for MOLGXP feature. The error rates are

further reduced for the fusion method which combines the scores from the

matchers of MOLGXP and PCA using sum rule for identification.

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Figure 3.12 Error Trade off Curves for Palmprint Recognition systems

3.7 RESEARCH CONTRIBUTIONS

In this chapter the palmprint recognition system is implemented

and the research contributions are as follows

In palmprint recognition, the preprocessing stage involves

segmenting a specific portion from the central palm area that

is invariant to rotation. The stable keypoints are used for

extracting the ROI.

Most of the existing works that make use of Gabor filters for

feature extraction varied the factors like orientation, scale and

frequency. In this work, the Gabor filter is designed for

different orientation only keeping the other factors constant.

This reduces the number of computations but effectively

extracts the texture feature features.

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The existing works make use of the magnitude response of

Gabor filter for feature encoding which is dependent on the

contrast values of the image pixels. This deteriorates the

performance of the biometric system. In the proposed work

only the phase values are used for encoding the feature which

is independent of the contrast.

The encoding technique is performed in two steps,

quantization and then coding. The quantization step serves to

make the encoding process more robust to phase changes

introduced by rotation of the palm during image acquisition.

Also the phase values in a 3×3 subimage are coded as a eight

bit code. In the existing works each phase value is encoded as

a two bit binary code which increases the storage

requirements.

The global features are extracted using Principal Component

Analysis (PCA).This serves to provide more discriminant

information than Fourier descriptors which make use of fixed

basis functions.

Extraction of both local and global features and fusion using

sum rule improves the performance. Also the matching is

performed based on distance measure which does not require

expensive training when making use of classifiers.

Advantages and Disadvantages: The advantages and the disadvantages of

the proposed palmprint recognition system is given below

The use of Gabor filter has an advantage of being more robust

to varying brightness and contrast of the images.

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The recognition rate is improved by using both local and

global information for feature representation.

The encoding method is more robust to rotational changes and

uses less number of bits for storing the feature vector.

The performance of the proposed Palmprint Recognition

system is better in terms of recognition rate and error rates.

The disadvantage is the added quantization step used in the

encoding process but this too serves to capture stable features.

3.8 COMPARATIVE ANALYSIS

In this section the performance of the proposed work is compared

with recent existing works that have made use of Gabor filter and PCA in

terms of recognition rate as most of the existing works have used the same to

evaluate the performance of the biometric systems.

From the Table 3.4 it is found that Lu et al (2009) have made use of

Gabor wavelets with eight orientations and five scales. Thus a total of

40(8×5) images are obtained. Both global and local covariance matrix of

Gabor magnitude and Gabor phase is stored as feature vector and sum rule is

used feature representation. A recognition rate of 98% is achieved

Ribaric & Marcetic (2012) have used Gabor filter to capture three

set of feature vectors for by varying parameters like orientations, frequency of

the sinusoid and standard deviation of the Gaussian envelope. Four different

orientations were used and the output from the Gabor filter consists of

12(4×3) images for the three spectral components. Also both real part and

imaginary parts of the outputs are encoded to represent the feature vector and

weighted sum rule is used for fusion.

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Similarly Xu et al (2012) has made use of multispectral palmprint

images of four different wavelengths red, blue green and NIR. DWT

coefficients and PCA are used for feature representation and weighted sum

rule for fusion.

From the above discussion it is observed that the existing methods

requires more number of computations, increased memory requirements and

also more features are extracted in comparison with the proposed method.

Also the recognition rates of these techniques are less compared with the

proposed method. Thus the proposed palmprint recognition method is

efficient.

Table 3.4 Comparison of proposed Palmprint Recognition system with Recent works

Author Feature Extracted Fusion Rule

ClassifierRecognition

Rate % Lu et al (2009)

Gabor magnitude and Gabor phase

Sum ruleEigen value based distance

98.00

Ribaric & Marcetic(2012)

Gabor feature of three spectral components (R,G,B)

Weighted Sum rule

Hamming distance 98.71

Xu et al (2012)

QPCA and QDWT Weighted Sum rule

Euclideandistance

98.83

Proposedmethod

MOLGXP +PCA Sum rule Euclidean distance

98.98

From the Table 3.4 it is observed that the proposed method

provides higher Recognition Rate in comparison the existing methods

discussed which have used more feature and involves more computations.

The reason for the improved performance is attributed by i) improved

preprocessing technique that uses stable keypoints for ROI extraction

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ii) additional quantization step used in the encoding process which makes the

generated feature vector code robust to changes in phase values caused by the

slight variations in palm movement.

3.9 SUMMARY

In this work, a Palmprint Recognition System based on LGXP and

Principal Component features is proposed. Different experiments have been

conducted. In the first case the LGXP feature for a single orientation is alone

considered. A recognition rate of 94.14%is achieved and it is improved to

96.5% when the MOLGXP feature is considered for six different orientations

with the features being concatenated. In the third case principal component

features are extracted and minimum matching scores from MOLGXP matcher

and PCA matcher are fused using the sum rule. This further improved the

recognition rate to 98.98%. Finally the comparative analysis is presented

where the proposed method is found to provide improved performance is

comparison with the existing techniques.