Successive Geometric Center Based Dynamic Signature Recognition

Signature Recognition using Successive Geometric

Centers, Grid and Texture and Soft Biometric Features

Pravin Jangid

Thakur/115

Under Guidance of

Dr. R.R Sedamkar

Thakur College of Engineering and Technology2013-14

2

Outline• Introduction• Literature Survey• Signature Recognition • Pre-processing of Signature• Successive Geometric Centers • Grid & Texture Information Feature Extraction • Results and Discussion• Conclusions and Future Scope• References• Publications

3

Introduction• Biometrics comprises methods for uniquely recognizing

humans based upon one or more intrinsic physical or behavioral traits.

• Biometric characteristics can be divided in two main classes:

• Physiological are related to the shape of the body.

• Behavioral are related to the behavior of a person.

4

Physiological Biometric Traits

Fingerprint Palmprint Finger Knuckle Prints

Face Iris

Other examples are Hand Vein, Hand Geometry, Facial Thermogram, Retina, DNA, Ear Geometry, Body Odour.

5

Behavioral Biometric Traits

Dynamic Signature Keystroke Dynamics

Other Examples are Speech, Gait, Facial Emotions

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Performance Metrics• False Accept Rate (FAR) or False Match Rate

• False Reject Rate (FRR) or False Non-Match Rate

• Equal Error Rate (EER) • Performance Index (PI)

• PI=100-EER

0 75 150

225

300

375

450

525

600

675

750

825

900

975

1050

1125

0

10

20

30

40

50

60

70

80

90

100

FRR

FAR

EER= 05 %PI =95%

Threshold

% R

ate

FAR Vs. FRR Plot

Total Number Imposter Signatures Accepted as Genuine

Total Number of Forgery Tests PerformedFAR

Total Number Genuine Signatures Rejected as Imposter

Total Number of Genuine Matching Tests PerformedFRR

7

Performance Metrics• True Acceptance Rate (TAR)

• True Rejection Rate (TRR)

Total Number Genuine Signatures Accepted

Total Number of Genuine Matching Tests PerformedTAR

Total Number Imposter Signatures Rejected

Total Number of Forgery Tests PerformedTRR

8

Biometric Authentication Systems Explored

Biometric Authentication

Systems

Unimodal

Physiological

Hand Based – Fingerprint,

Palmprint, FKP

Face & Iris

Behavioral

Dynamic Signature

Keystroke Dynamics

Multimodal

Multi-Algorithmic

Multi- Instance

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Problem Statement• Dynamic signatures with x, y, pressure information is

considered here for recognition purpose. • Main focus of research is to use successive Geometric

Centers of depth-2 for pressure distribution analysis of the dynamic signatures.

• Along with above mentioned feature Grid and Texture feature are extracted and their performance is analyzed.

• Soft biometric feature such as number of pixels, arc length, Signature timing is also added for analyzing their feasibility in improving the accuracy of recognition.

• Final step is to analyze the performance of the proposed method for biometric authentication.

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Problem Statement• TAR-TRR analysis will be performed for intra-class as well

as inter-class testing.• Performance metrics such as Performance Index (PI),

Security Performance Index (SPI) will be used for evaluation and final validation will be performed by comparing the results with existing systems.

Literature SurveySr. No.

Name of the Paper

Authors Publication detail Abstract Issues

1 Novel Features for Offline Signature verification

Banshider Majhi

International Journal of Computers, Communications & Control ,Vol. I (2006), No. 1, pp. 17-24.

The concept is used offline for successive geometric centers of depth -1.Euclidean distance model was used for classification.

TAR is 61.12 and TRR is 78 which is less.

2 Offline Handwritten Signature Verification Using Radial Basis Function Neural Networks

George Azzopardi Kenneth P. Camilleri

International Journal of Computer Applications , Vol. 2 ,No. 3,pp.74-80, May 2010

A signature database is collected using intrapersonal variations for evaluation. Global, grid and texture features are used as feature sets.

Implementation is offline.TAR and TRR can be improve by implementing online.

3 Performance Analysis of Succesive Geometric centers of depth 2

Dr. H.B. Kekre,V A Bharadi

IJJCCT 2009, Shanghai, China, Dec 2009

The concept is used offline for successive geometric centers of depth -2.

TAR is 65.45 and TRR is 82 which is less and can be improve.

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Architectures of the System

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Signature Recognition• Dynamic Signatures are explored as behavioral

Biometrics• Digitizer Tablet is used for capturing dynamic signatures.

Digitizer Tablet for Online Signature Scan

Feature Plot for Multidimensional features X,Y,Z Co-ordinates, Pressure Azimuth & Altitude parameter

Signature Samples of a Person (a) Static Scanned Signature, (b) Dynamic Signature Scanned by Wacom Intuos Pressure Levels for the Dynamic Signatures Shown

(a) (b)

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Steps in Dynamic SRS• Capture Data • Preprocessing• Feature Extraction • Matching

• Preprocessing is important as the captured Signatures has been sampled at finite sampling rate

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Preprocessed Signature & Extracted Features

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Successive Geometric Centers • This parameter is derived from the center of mass of an

image segment.• We split the template and find the geometric center of

mass of each generated segment and again split the template at the center of mass.

• This process if repeated 15 times to generate 24 points & carried over in two modes horizontal & Vertical splitting.

• In one level we split the template 3 times & obtain 6 points & 4 segments, these 4 segments are split again and to generate total 6*4= 24 points. Hence the name successive geometric centers of depth 2.

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Successive Geometric Centers – Depth1

Horizontal Splitting Vertical Splitting

maxmax

1 1

maxmax

1 1

[ , ]

[ , ]

x

yxx b x y

x yC yx

b x yx y

maxmax

1 1

maxmax

1 1

[ , ]

[ , ]

y

yxy b x y

x yC yx

b x yx y

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Successive Geometric Centers – Depth2

Vertical Splitting

Horizontal Splitting

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Classification using Geometric centers• We obtain total 48 feature points (v1;;;v24 and h1;;h24). This set of point

is can be used for comparing two signatures.• For comparison purpose we use coefficient of Extended Regression

Square (ER2) .• Defined as R-squared is also called the coefficient of determination. It

can be interpreted as the fraction of the variation in Y that is explained by X. R-squared can be further derived as:

n= Number of dimensions (For current scenario we have two dimensions)

xi- Points for first sequence , yi- Points for second sequence

Where X, Y are two sequences to be correlated each of two dimensions.• For perfect regression the value of ER2 is 1, hence for signature from the

same person the value should be close to 1.

2

1 12

2 2

1 1 1 1

M n

ji j ij jj i

M n M n

ji j ji jj i j i

x X y Y

ERx X y Y

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Grid & Texture Information Features• Grid and texture feature provide information about the

distribution of pixels and the distribution density of the pixels

• Grid feature provides information about pixel density

• Texture feature provide information about the occurrence of specific pattern in the signature template

• These features are not based on single pixel or whole signature but they are based on group of pixels or signature segments, hence these are cluster features

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Grid Information FeatureAlgorithm for grid feature extraction from signature template of size

256*256 pixels

1. Divide the skeletonized image into 10 X 10 Pixels blocks.

2. For each block segment, calculate the area (sum of pressure pixels). This gives a grid feature matrix (gf) of size 25 X 25

3. Find minimum and maximum (min, max) values for pixels block. Ignore blocks with no pixels.

4. Normalize the grid feature matrix by replacing each nonzero element ‘e i j’ by

This gives matrix with all elements within the range of 0 and 1.

5. The resulting 625 elements of the matrix (gf) form the grid feature vector

( min)

max min

ijij

eg

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Grid Information Features

(a) (b)

Grid Information Feature with Pressure Matrix (a) Original Online Signature Template with Corresponding Pressure Levels (b) Grid Feature Matrix Representation

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Grid Information Features

The Grid Feature Matrix for the signature

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Texture Feature• Texture feature [12] gives information about occurrence of

specific pen tip pressure pattern while signing.• Originally it is proposed for a pair of pixel values , here it

is extended to pressure values.• The five different pressure levels are R1 = (0-20), R2 =

(21-40), R3 = (41-60), R4 = (61-80) and R5 = (81-100)• The signature template is first scanned and divided in to 5

different sub-templates.• Each template has signature points having pressure

levels given by one of the five ranges R1 to R5.

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Texture Feature• Each sub template can be treated as a simple template

with signature points as black and background as white level.

• To extract the texture feature group, the co-occurrence matrices of the sub-template are used.

• In a sub- template, the co-occurrence matrix C [i, j] is defined by first specifying a displacement vector d = (dx, dy) and counting all pairs of pixels separated by d and having pressure level values Pi and Pj.

• In current case, the signature image is binary and therefore the co-occurrence matrix is a 2 X 2 matrix describing the transition of black (Pressure Pi) and white (Pressure Pj) pixels.

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Texture Feature• Therefore, the co-occurrence matrix C [i, j] is defined as• Where c00 is the number of times that two white pixels

occurs, separated by d. c01 is the number of times that a combination of a white and a black pixel occurs, separated by d. c10 is the same as c01. The element c11 is the number of times that two black pixels occur, separated by d.

• The image is divided into sixteen rectangular segments (4 X 4) as shown

• For each region the C (1, 0), C (1, 1), C (0, 1) and C (-1, -1) matrices are calculated and the c01 and c11 elements of these matrices are used as texture features of the signature

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Texture Feature• The Pixel positions while scanning for the displacement

vector are as follows

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Algorithm for texture feature extraction

1. Scan the signature pressure template, Divide the template into five sub-template with pressure level range R1 = (0- 20), R2 = (21-40), R3 = (41-60), R4 = (61-80) and R5 = (81-100). Start with Template R1.

2. Define the displacement vector. d= (dx, dy).

3. Start scanning the signature sub-template segments 1 to segment 16.

4. Find the occurrence of pixel sets c00, c01, c10 & c11 for the displacement vector dx, dy for the segments defined in step 3. Where 0 indicates pressure level P1 and 1 indicates Pressure Level P2

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Algorithm for texture feature extraction

5. Repeat the procedure for all the 16 segments, and all pressure ranges. Store the values in specific memory structure.

6. When all the segments are scanned, select other displacement vector and repeat steps 3 to 5. This is repeated for all four displacement vectors (1,0),(1,1),(0,1),(-1,-1).

7. Select element c01 and c11 of the following matrix for each displacement vector.

8. This procedure gives total 128 X 5 = 320 elements as follows (16 segments X 4 matrices X 2 elements per matrix X 5 Ranges). This forms the texture feature vector. Fig. shows TFD 1 to TFD 5 for the signature shown in Fig. 2. These matrices are used as feature vector and signatures are matched using them. Euclidean distance based matching is done.

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Texture Feature

TXC TFD1 TFD2

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Texture Feature

TFD3 TFD4 TFD5

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Soft Biometric

• Soft Biometrics is defined as a set of traits providing information about an individual.

• Traits which accept the above definition include, but are not limited to:

User Signature with Soft Biometrics Features

Sign1 Sign 2 Sign 3

No of Pixels 563 461 1080

Arch Length 978 567 1746

Slope Angle 38.81 21.03 3.32

Base Line Shift 37.0 15.0 5.0

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Soft Biometric

• Physical: skin colour, eye colour, hair colour, and presence of beard, presence of moustache, height, and weight.

• Behavioral: gait, keystroke, Signature length, pixel counts etc.

• Adhered human characteristics: clothes colour, tattoos, accessories.

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Enrollment & Training • Signature Recognition is a two class pattern recognition.

Signature is to be classified as genuine or forgery.

• Total 108 signatures

• Goal is to calculate decision thresholds based on the intra-class variation

• We use Euclidian Distance Model for classification.

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Screenshot for Geometric Centers

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Screenshot for Grid Features

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Results For Geometric Centers Depth -2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 4740

50

60

70

80

90

100

TAR TRR Plot for Unimodal - GCH, GCV

GCH-TAR GCH-TRR GCV-TAR GCV-TRR

TAR-TRR Plot for GCH & GCV Showing EER = 88.33 (GCH) & 87.14 (GCV).

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Results For Geometric Centers Depth -2 with Soft Biometric

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 4740

50

60

70

80

90

100

Geometric Centers Depth-2 with Soft Biometric FV- GCH ,GCV

GCH-TAR GCH-TRR GCV-TAR GCV-TRR

GCH-SB-TAR GCH-SB-TRR GCV-SB-TAR GCV-SB-TRR

TAR-TRR Analysis for (a) Geometric Centers Depth-2 with Soft Biometric Features showing EER = 88.33 (GCH) & 87.14 (GCV) and EER (With SB) = 91.37 (GCH-SB) & 91.18 (GCV-SB)

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Results

GCH - Online GCV - Online GCH - Off-line GCV - Off-line

PI 88.33 87.14 83.14 81.22

SPI 9.1 11.11 5.29 7.9

5152535455565758595

Performance Comparison of Online & Offline Systems for Successive Geometric Center based Feature Vectors

% P

I & S

PI

Performance Comparison for Successive Geometric Centers of Depth II, both Online & Offline Variants

Parameter Geometric centers Depth-2Geometric centers Depth-2 with Soft

Biometric

PI GCH(88.33) GCV(87.14) GCH(91.37) GCV(91.18)

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Result For Grid Features

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 9640

60

80

100

Grid FV along with SoftBiometric

GF-TAR GF-TRR GF-SB-TAR GF-SB-TRR

(a) (b)

TAR-TRR Analysis for (a) Grid Showing EER = 69.25 (b) Grid with Soft Biometric showing EER=75.73

Parameter Grid Features Grid Features with Soft Biometric

PI 69.25 75.73

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 970

20

40

60

80

100

TAR TRR Plot for GF

GF-TAR GF-TRR

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Result For Texture Features

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 970

20

40

60

80

100

120

Texture FV

TXC-TAR TXC-TRR

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 4640

50

60

70

80

90

100

Texture FV along with Soft Biometric

TXC-TAR TXC-TRR

TXC-SB-TAR TXC-SB-TRR

(a) (b)

TAR-TRR Analysis for (a) Texture Showing EER = 75.73 (b) Texture with Soft Biometric showing EER=85.14

Parameter Texture Features Texture Features with Soft Biometric

PI 75.73 85.14

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Result For TFD1

TAR-TRR Analysis for (a) TFD1 Showing EER = 82.70 (b) TFD1 with Soft Biometric showing EER=85.55

Parameter TFD1 TFD1 with Soft Biometric

PI 82.70 85.55

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445460

20

40

60

80

100

120TFD1 along with Soft Biometric

TFD1-TAR TFD1-TRR TFD1-SB-TAR TFD1-SB-TRR

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Result For TFD2



PI 60.43 86.03

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445460

20

40

60

80

100


TFD1-TAR TFD1-TRR TFD1-SB-TAR TFD1-SB-TRR1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647

0

20

40

60

80

100

120

TFD2 along with Soft Biometric


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Result For TFD3



PI 55.84 85.40

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445460

20

40

60

80

100



1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334353637383940414243444546470

20

40

60

80

100

120



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Result For TFD4



PI 63.95 84.70

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445460

20

40

60

80

100


TFD1-TAR TFD1-TRR TFD1-SB-TAR

1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647480

20

40

60

80

100

120



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Result For TFD5



PI 71.29 84.44

0

20

40

60

80

100


TFD1-TAR TFD1-TRR

TFD1-SB-TAR TFD1-SB-TRR

1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647480

20

40

60

80

100

120



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Result For Texture Features

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 970

20

40

60

80

100

120

Texture FV

TXC-TAR TXC-TRR

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 4640

50

60

70

80

90

100

Texture FV along with Soft Biometric

TXC-TAR TXC-TRR

TXC-SB-TAR TXC-SB-TRR

(a) (b)

TAR-TRR Analysis for (a) Texture Showing EER = 75.73 (b) Texture with Soft Biometric showing EER=85.14

Parameter Texture Features Texture Features with Soft Biometric

PI 75.73 85.14

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Result Analysis

Without Soft Biometrics

With Soft Biometrics

Offline SystemPerformance

Feature Vector Type PI SPI PI SPI PI SPI

GCH 88.33 9.09 91.37 18.18 83 6.0

GCV 87.14 11.11 91.18 20.00 81 5.2 Grid Feature 69.25 32 85.03 25 82 2.1

TXF 75.73 14.81 85.14 25 81 6.1

TFD1 82.7 15.79 85.55 30 NA NA

TFD2 60.43 40 86.03 14.29 NA NA

TFD3 55.84 14.29 85.4 14.29 NA NA

TFD4 63.95 5.24 84.7 14.29 NA NA

TFD5 71.29 14.29 84.44 14.29 NA NA

(GCH: Geometric Centers Horizontal Feature Vector, Geometric Centers Vertical, TXC- Texture Feature Vector)

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Software and Hardware RequirementHardware Requirement:• Processor : Intel(R) Core(TM) 2 Duo CPU @ 2.00

GHz• RAM : 2.00GB or more• Hard disk : 40GB or more• Digitizing Tablet

Software Requirement:• Operating System : Windows XP• Windows edition : Windows 8 Pro © 2012 Microsoft

Corporation.• Programming Language : C# .Net• Development Kit : Visual studio 2013

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Conclusion• The proposed method was tested on 108 users, total 2700 Genuine

(Intra Class) & 2,88,900 (Inter Class) Tests were performed• In our research it is observed that the Successive Geometric Centers

of Depth II achieve 88.33(GCH) & 87.14(GCV) % PI for dynamic signatures as compared to 83.14 & 81.22% PI of Static implementation.

• Online signature recognition system gives more accuracy.• Dynamic signature based system gives best performance amongst

behavioral biometrics. Research suggests that online signature based systems are more accurate than static systems as they consider dynamic nature of the signature. Forgery detection which is difficult in static system can be easily done in the dynamic systems.

• For comparing the performance TAR-TRR analysis based Performance Index (PI) and Security Performance Index (SPI) are used.

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Conclusion• The EER for Grid Features was 69.25% and that of Texture

features is 75.73%.• Performance is increased when the soft biometric features

such as Number of Pixels (NOP), Dominant Angle, Baseline Shift (BLS) are added to the feature vector.

• It is clearly seen that the Online-signature based feature vector along with soft biometric features gives the best performance of PI =86.03% for TFD 2 an PI = 85.55% for TFD 1. The grid features give PI = 85.03%.

• The static signature based system has less results of 82 & 81 % respectively.

• The proposed system has achieved up to 86.03% of PI and 40% of SPI for texture Feature, as compared to 81% of PI & 6.1% SPI of static signature recognition system.

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Further Work• For better performance Successive Geometric Centers of

Depth-3 can be use.

• Mainly K-NN classifier is used for classification in unimodal as well as multimodal implementations. This is a simple classifier with limited scope for training. The classification accuracy can be improved by incorporating SVM (support vector machine)classifier.

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Summary of Publications• Dr. R. R. Sedamkar, Mr. P. S. Jangid, “Dynamic Signature

Recognition using Successive Geometric Center Level 2 based Feature Vector Extraction”, ELSEVIER, Vol. I (2014), No. 1, pp. 17-24.

• Dr. R. R. Sedamkar, Mr. P. S. Jangid,“Performance Analysis of Grid & Texture based Feature Vector for Dynamic Signature Recognition”, ELSEVIER, Vol. I (2014), No. 1, pp. 27-37.

54

Summary of PublicationsSr. Paper Publication Summary

Total Publications on M.E. Research 02

1. International Conference Papers

ELSEVIER 01

IEEE 01

55

References• H. Dullink, B. van Daalen, J. Nijhuis, L. Spaanenburg, H. Zuidhof, “Implementing a DSP

Kernel for Online Dynamic Handwritten Signature Verification using the TMS320 DSP Family”, EFRIE, France December 1995 SPRA304.

• A. K. Jain, A. Ross, and S. Prabhakar, “On Line Signature Verification”, Pattern Recognition, vol. 35, no. 12, pp. 2963-2972, Dec 2002.

• H. lei, S. Palla and V Govindraju, “ER2: an Intuitive Similarity measure for On-line • Signature Verification”, Proceedings of CUBS 2005. • K.Tanabe, M.Yoshihara, H.Kameya and S.Mori, “Automatic Signature Verification Based

on the Dynamic Feature of Pressure”, IEEE Conf., 10-13 Sept. 2001, 10.1109/ICDAR.2001.953945

• Abdullah I. Al-Shoshan, “Handwritten Signature Verification Using Image Invariants and Dynamic Features”, Proceedings of the International Conference on Computer Graphics, Imaging and Visualization (CGIV'06) ,March 2006, 0-7695-2606

• J. Hasna, “Signature Recognition Using Conjugate Gradient Neural Networks”, IEEE transactions on engineering, computing and technology, Vol. 14, august 2006, ISSN 1305-5313

• V. Nalwa, “Automatic On-Line Signature Verification”, proceedings of the IEEE Transactions on Biometrics, vol. 85, No. 2, February 1997.

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References

• J Edson, R. Justino, F. Bortolozzi and R. Sabourin, "An off-line signature verification using HMM for Random, Simple and Skilled Forgeries", Sixth International Conference on Document Analysis and Recognition, pp. 1031-1034, Sept.2001.

• Bai-ling Zhang, “Off-line Signature Recognition and Verification by Kernel Principal Component Self-regression”, Proceedings of the 5th International Conference on Machine Learning and Applications (ICMLA'06), 0-7695-2735-3/06, 2006

• H. Baltzakis, N. Papamarkos, “A new signature verification technique based on a two-stage neural network classifier”, Engineering Applications of Artificial Intelligence 14 (2001) 95±103, 0952-1976/01/$ - PII: S 0 9 5 2 - 1 9 7 6 (0 0) 0 0 0 6 4 - 6

• S. Armand, M. Blumenstein and V. Muthukkumarasamy, “Off-line Signature Verification based on the Modified Direction Feature”, Engineering Applications of Artificial Intelligence 14 (2004), 0952-1976/04/$ - PII: S 0 9 5 2

• B. Majhi, Y. Reddy, D. Babu, “Novel Features for Off-line Signature

Verification”, International Journal of Computers, Communications & Control Vol.

I (2006), No. 1, pp. 17-24.• M.K kalera, S. Shrihari, “Offline Signature Verification And Identification Using Distance Statistics”, International

Journal of Pattern Recognition and Artificial Intelligence Vol. 18, No. 7 (2004) 1339-1360 ,World Scientific Publishing Company

• S. Chen and S. Srihari, “Use of Exterior Contours and Shape Features in Off-line Signature Verification”, Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR’05), 1520-5263/05

• Y. Xuhua, F. Takashi, K. Obata, Y. Uchikawa, “Constructing a High Performance Signature Verification System Using a GA Method”, IEEE Conf. ANNES, 20-23 Nov. 1995, PP: 170 - 173, 10.1109/ANNES.1995.499465,

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Questions ?

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Thank You !!