Signature Recognition using Clustering Techniques

ByVinayak Ashok BharadiM E EXTC TSEC

Guided ByDr. H B KekreProf. Computer DepartmentTSEC

Dissertation Seminar

IndexWhy Signature Recognition?

Problem Definition

Pre-processing of Signature

Global Feature extraction

Grid & Texture Information Feature Extraction

Vector Quantization a Clustering Technique

Walsh coefficients

Successive Geometric Centers as a Global Feature

Results

Conclusion

Why Signature Recognition?

Main Application- Banking & E-commerce

Document Authentication Cheque, Wills, Official Documents

Signature is an attribute used for decade for document authentication.

Least user co-operation required.

On-Line as well as off-line modes are possible.

Signature Verification can be addressed as a problem in signal processing.

Image processing techniques can be used.

Problem DefinitionSignature Recognition Classified in two categories

1. On-line Signature Recognition

2. Off-Line Signature Recognition

Steps in Signature Recognition

1. Data Acquisition

2. Pre-processing Noise removal, Intensity

Normalization, Resizing, Thinning.

3. Feature Extraction

4. Enrollment & Training

5. Performance Evaluation

Performance Evaluation- Detection of different levels of forgeries. Performance Evaluation by FAR, FRR, CCR etc.

Signature Recognition using Clustering TechniquesClustering techniques Signature Recognition is using Cluster

features along with other feature set

Cluster Based Features

1. Codeword Histogram of a signature template &

their Spatial Moments.

2. Grid & Texture Information feature

Special Features-

1. Walsh Coefficients of Pixel Distributions

2. Successive Geometric Centers of Depth 2

Steps in Signature Recognition

Pre-Processing Demo

Features of Signature template Global Features Standard Global Features Special Features Local Features Pressure points, Velocity, Acceleration, Moments, Slope, Angle

Feature Extraction

Standard Global FeaturesIn the program we consider a Normalized signature template of dimensions 200 X 160 pixels.

We consider following global features.

1. Number of pixels Total Number of black pixels in a signature template

2. Picture height - The height of the signature image after vertical blank spaces removed.

3. Picture width- The width of the image with horizontal blank spaces removed

4. Maximum horizontal projection- The horizontal projection histogram is calculated

and the highest value of it is considered as the maximum horizontal projection .

5. Maximum vertical projection- The vertical projection of the skeletonized signature

image is calculated. The highest value of the projection histogram is taken as the

maximum vertical projection .

6. Dominant Angle -dominant angle of the signature, angle formed by the center of

masses with the baseline of the signature.

7. Baseline shift- This is the difference between the y-coordinate of centre of mass of left

and right part. We calculate this by calculating the center of mass of left and right part

of the signature. The difference between y co-ordinates of the center of masses is the

baseline shift.

This is a parallel feature to the dominant angle but gives extra information about the

signatures. Two signatures may have same dominant angle but at the same time they may

have different baseline shift. This helps for achieving classification accuracy.

8. Signature surface area here we consider the modified tri-area feature .

Area Generation Results

Original Algorithm Modified Algorithm

Area1 Area2 Area3 Area1 Area2 Area3

1 0.1108 0.1823 0.0542 0.1699 0.2565 0.1066

2 0.0593 0.1809 0.1457 0.0815 0.1951 0.1571

3 0.0489 0.0785 0.0570 0.1040 0.1400 0.1121

Modified AlgorithmOriginal Algorithm

Area Generated for signatures

Global Feature Vector

Sr. FeatureExtracted

Value1 Number of pixels 547

2 Picture Width (in pixels) 166

3 Picture Height (in pixels) 137

4 Horizontal max Projections 12

5 Vertical max Projections 15

6 Dominant Angle-normalized 0.694

7 Baseline Shift (in pixels) 47

8 Area1 0.151325

9 Area2 0.253030

10 Area3 0.062878

Signature Template

Feature Extracted from the signature

Special Features We are considering following special features of the signature

1. Grid & Texture Information Features

2. Walsh coefficients of horizontal and vertical pixel projections

3. Codeword Histogram & Spatial Moments of codewords

4. Successive Geometric Centers of Depth 2

Grid Information Features

Representation of the grid feature vector of a signature (a) Original Signature (b) Normalized Signature (c) Representation of grid feature.

Grid Information Features

(a)

(b)

The Grid Feature Matrix for the signature (a) Normalized Matrix (b) Original Pixel Values

Texture FeatureTexture feature gives information about the occurrence of

specific pixel pattern

We use a coarser segmentation method, divide the template in 8 segments

To extract the texture feature group, the co-occurrence matrices of the signature image are used

In a grey-level image, 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 grey level values i and j

In our case, the signature image is binary and therefore the co-occurrence matrix is a 2 X 2 matrix describing the transition of black and white pixels.

In a grey-level image, 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 grey level values i and j

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 [d=(dx, dy)]

The image is divided into eight rectangular segments (4 X 2).

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.

Texture Feature

00 01

10 11[ , ]C i j

c ccc

=

The Pixel positions while scanning for the displacement vector are as follows

We get a matrix having total 64 elements as the feature vector. (2 Elements X 4 matrices X 8 segments)

Texture Feature

Application of VQ for Signature Recognition

Application of VQ for Signature Recognition

Codebook Generation

Codeword Generation Codebook OptimizationCodeword Grouping Codebook Plays important role in codeword histogram generation. We divide this process in three parts

Codebook Optimization - Sorting

This helps for forming codeword groupIn this stage we first rearrange the codewords so that the two consecutive codewords are similar (having less hamming distance).

In this stage we first rearrange the codewords so that the two consecutive codewords are similar (having less hamming distance).

This helps for forming codeword group

Initial Codebook

We have total 11755 codewords, to form the codeword histogram we form codeword groups.

various combinations are tried in software code. Here we present grouping of 12 codewords to form total 980 groups.

The participants of group are codewords with minimum intra group hamming distance and hence they represent a set of similar blocks and hence similar signature template segments.

We use this codeword groups to generate codeword Histogram.

Codeword Grouping

Codeword Grouping

Codeword Groups formed after grouping process

Adding Spatial Moments

We also add the spatial information about the codewords. This is done by calculating moments for each codeword group.

We find moments of gravity(G) and inertia (I).

1

1 Mx i

iG x

M ==

1

1 Ny i

iG y

N ==

2

1

1 nx i

iI x

M == 2

1

1 ny i

iI y

M ==

We have to total 1960 (980 for G + 980 for I) elements for the codeword histogram of the signature template.We use codeword histogram and associated moments as a feature set of the signature template.

Classification using VQ

We have sequence of numbers as parameters. We have codeword histogram as an array of 980 elements.

Two arrays of moment of gravity and inertia(G & I). To evaluate similarity between such sequences we use a Euclidian distance based formula.

The feature vector for signature template I1 and the feature vector for test signature I2 are given below,

I1= {W11, W21, WN1} , I2= {W12 , W22 , WN2 }

In the histogram model, Wij = Fij , where Fij is the frequency of group Ci appearing in Ij

The feature vectors I1 and I2 are the codeword histograms

Similarity Score

The similarity measure S is defined as

Where the distance function (dis(I2,I1)) is

This formula is used to evaluate the similarity between two codeword Histograms, to evaluate the similarity between spatial information we use simple Euclidian distance.

1

| 1 2 |( 2, 1)1 1 2

N

i

Wi Widis I IWi Wi=

=+ +

1( 2, 1) 1 ( 2, 1)

s I Idis I I

=+

Walsh Coefficients of Pixel Distributions These are another set of global features proposed in

this project.

Rather than matching the distributions directly we match their interpret these distributions as signals and match their Walsh coefficients.

First we generate Hadamard coefficients by multiplying the pixel distribution values by Hadamard matrix.

We have a signature template 0f 200 x 160 (transferred to 256 x 256 window) pixels and Hadamard matrix of 256 x 256.

Then a Hadamard matrix of order 256X256 is used to transform the coefficient of horizontal and vertical pixel distributions HP (i), VP (i)

HCH(i)= n HD(n)*HP(n) i=0,1,.255 (Hadamard Coeff. Horizontal)

HCV(i)= n HD(n)*VP(n) i=0,1,.255 (Hadamard Coeff. Vertical)

These coefficients are not sequency ordered, we arrange these coefficients using kekres Algorithm. this yields the Walsh Hadamard transform (WHT)

Walsh-Hadamard Transform

Kekres AlgorithmThis algorithm gives the sequence of numbers according to which the Hadamard coefficients can be

arranged so that we obtain Walsh coefficients. The algorithm is discussed as follows we consider 16 coefficients

Step 1:

Arrange the n coefficients in a row and then split the row in n/2, the other part is written below the upper row but in reverse order as follows

Step 2:

We get two rows, each of this row is again split in n/2 and other part is written in reverse order below the upper rows

This step is repeated until we get a single column matrix which gives the ordering of the Hadamard coefficients according to sequency as given below:

0 ,15, 7, 8, 3,12,4,11,1,14,6,9,2,13,5,10Step 3:

According to this sequence the Hadamard coefficients are arranged to get Walsh coefficients. We get WCH(i), WCH(i) (Walsh Coefficients Horizontal & Vertical) i=0 to 255 from HCH(i) & HCV(i).

0 1 2 3 4 5 6 715 14 13 12 11 10 9 8

0 1 2 3 4 5 6 715 14 13 12 11 10 9 8

7 6 5 4

8 9 10 11

Walsh Coefficients of Pixel Distributions

Successive Geometric Centers Depth1

Horizontal Splitting Vertical Splitting

maxmax

1 1maxmax

1 1

[ , ]

[ , ]x

yxx b x y

x yC yxb x y

x y

= =

= =

=

maxmax

1 1maxmax

1 1

[ , ]

[ , ]y

yxy b x y

x yC yxb x y

x y

= =

= =

=

Successive Geometric Centers Depth2

Horizontal Splitting

Vertical Splitting

Enrollment & Training

Enrollment of Users Signatures

Sr. Feature 1 2 3 4 5 6 7 8

1 Number of pixels 547 545 563 588 527 534 588 5482 Picture Width (in pixels) 166 168 173 174 155 168 169 1623 Picture Height (in pixels) 137 136 134 137 135 137 131 1384 Horizontal max Projection 12 14 13 15 12 15 13 155 Vertical max Projection 15 13 14 18 13 12 16 136 Dominant Angle-normalized 0.6947 0.6882 0.6801 0.6902 0.6988 0.6923 0.6810 0.69027 Baseline Shift (in pixels) 47 47 47 49 49 49 46 498 Area1 0.1513 0.1329 0.1362 0.1337 0.1062 0.1170 0.1508 0.11809 Area2 0.2530 0.2250 0.2369 0.2264 0.2275 0.1955 0.2218 0.188010 Area3 0.0629 0.0656 0.1237 0.0764 0.0938 0.0536 0.0501 0.1006

Global Feature vectors of training signatures of a person

Medians & Threshold ValuesSr. Feature Median Threshold1 Number of pixels 547 41.75332 Picture Width (in pixels) 168 9.63543 Picture Height (in pixels) 136 3.62184 Horizontal max Projection 13 2.17805 Vertical max Projection 14 3.48816 Dominant Angle-normalized 0.69021 0.01167 Baseline Shift (in pixels) 47.0000 2.16068 Area1 0.133712 0.02719 Area2 0.22642 0.026710 Area3 0.065625 0.042211 Walsh H Distance 434.433 119.117412 Walsh V Distance 600.1525 94.573213 Grid Distance 281.0818 62.186614 Texture Distance 62.14499 33.639815 Vector Quantization S-Score 3.484029 0.506516 Vector Quantization F-ED 16.91153 3.589417 VQ SP Moment Gravity GX 151.9263 13.202418 VQ SP Moment Gravity GY 132.6735 11.996119 VQ SP Moment Inertia IX 5325.065 491.873620 VQ SP Moment Inertia IY 3765.733 413.791921 Geometric center HX - 48.011422 Geometric center HY - 39.529623 Geometric center VX - 46.060424 Geometric center VY - 29.9552

Results-Classification

Result -Signature Verification

(a) (b)

(c) (d)

Result-Signature RecognitionSignature Recognition Result - 6/9/2007 8:37:27 PM Maximum match = 73.31 found for UID 1 and the Signature is ACCEPTED, Authentic user.

Performance Analysis

400

100060

080

012

0014

0016

0018

000

20

40

60

80

100

120

Walsh Coefficients FRR FAR

FRR

Threshold

% Acceptance

EER=40%

60 75 80 85 90 95 100 105 110 115 1200

10

20

30

40

50

60

70

80

Geometric Centers FAR FRR

FRR

Threshold

% Acceptance Ratio

EER=16%

Sr. Parameter Value

1 FAR 50.00%

2 FRR 31.67%

3 TAR 68.33 %

4 TRR 50.00%

5 CCR 59.17%

6 FCR 41.83%

Performance Metrics for Walsh Coefficients

Sr. Parameter Value

1 FAR 05.45%2 FRR 34.55%3 TAR 65.45 %4 TRR 94.55%5 CCR 80.00%6 FCR 20.00%

Performance Metrics for Geometric Centers

100

150

200

250

300

350

400

450

500

0

20

40

60

80

100

120

Grid Feature FAR FRR

FARFRR

Threshold

% Acceptance

EER=18%

20 70 120

170

220

270

320

370

420

0

20

40

60

80

100

120

Texture Feature FAR FRR

FAR

Threshold

% Acceptance

EER=19%

Performance Analysis

Sr. Parameter Value

1 FAR 24.00%

2 FRR 06.67%

3 TAR 93.33 %

4 TRR 76.00%

5 CCR 84.67%

6 FCR 15.33%

Performance Metrics for Grid Features

Sr. Parameter Value

1 FAR 24.00%

2 FRR 17.33%

3 TAR 82.67 %

4 TRR 76.00%

5 CCR 91.33%

6 FCR 8.67%

Performance Metrics for Texture Features

Performance Analysis- VQ2.

5

2.7

2.9

3.1

3.3

3.5

3.7

3.9

4.09

9999

9999

9999

96 4.3

4.5

4.7

4.90

0000

0000

0000

04

0

20

40

60

80

100

120

S-Score FAR FRR

FRR

Threshold

% Acceptance

EER=22%

3400

4200

5000

5800

6600

7400

8200

9000

0

20

40

60

80

100

120

VQ-Moment of Inertia FAR FRR

FARFRR

Threshold

% Acceptance

EER=36%

1800

2200

2600

3000

3400

3800

4200

4600

5000

0

20

40

60

80

100

120

VQ-Moment of Gravity FAR FRR

FARFRR

Threshold

% Acceptance

EER=40%

10 12 14 16 18 20 22 24 26 28 30 32 34

0

20

40

60

80

100

120

Euclidian Distance FAR FRR

FAR

Threshold

% Acceptance

EER=21%

Performance AnalysisSr. Parameter VQS VQED SPMG SPMI1 FAR 20 32.85 41 37.152 FRR 24.14 12.86 39 35.723 TAR 72.85 87.14 61 64.284 TRR 64.28 67.15 59 62.855 CCR 68.57 77.14 60 63.576 FCR 31.43 22.86 40 36.42

VQS VQED SPMG SPMI0

102030405060708090

CCR FCR for VQ

CCR

Feature

parameter Value

VQS VQED SPMG SPMI05

1015202530354045

FAR FRR for VQ

FAR

Feature

Parameter Value

Performance Metrics for VQ-features

Performance Analysis- SRSTest Mode

Inputs Test Signatures

Accepted/ Rejected

SignaturesPerformance

Metrics %

Verification

Cases That Should be Accepted

152

Cases Actually Accepted

142 TAR 93.42

Cases Falsely Rejected

10 FRR 06.58

Cases That Should be Rejected

201

Cases Actually Rejected

195 TRR 97.50

Cases Falsely Accepted

06 FAR 02.50

Recognition

Cases That Should be Accepted

135

Cases Actually Accepted

131 TAR 97.04

Cases Falsely Rejected

04 FRR 02.96

Cases That Should be Rejected

122

Cases Actually Rejected

112 TRR 91.80

Cases Falsely Accepted

10 FAR 08.20

-20 -8 4 16 28 40 52 64 76 88 100

0

20

40

60

80

100

120Recognition Mode -FAR-FRR Plot FA

R

Threshold

% Acceptance

EER=6%

-54

-45

-36

-27

-18 -9 0 9 18 27 36 45 54 63 72 81 90 99

0

20

40

60

80

100

120Signature Verification-FAR-FRR Plot FAR

Threshold

% Acceptance

EER=3.29%

The above mention entries indicate that out of total 610 tests conducted 580 tests gave correct classification and 30 test were failed hence the overall accuracy reported is 95.08%.

Performance Analysis- SRS

Sr. ParameterVerification

ModeRecognition

Mode

1 FAR 02.50 08.20

2 FRR 06.58 02.96

3 TAR 93.42 97.04

4 TRR 97.50 91.80

5 CCR 95.46 94.55

6 FCR 04.54 05.45

Performance Metrics in percentage for Signature Recognition System

Test Samples RatioResults

obtained on the given test

bed

All sample

of a subject

GenuineTAR 93.42

FRR 06.58

Forged

CasualFAR 00.00

TRR 100.00

SkilledFAR 05.60

TRR 94.40

Performance Metrics for Final System

Sr Feature FAR FRR1 Walsh Coefficients 40% 42%2 Vector Histogram 12% 22%3 Grid Feature 8% 12%4 Texture Feature 14% 20%5 Final System 2.5% 6.5%

Performance Metrics for features Extracted

Performance ComparisonSr. Approach FAR FRR Accuracy

1 Signature Recognition using Clustering Technique 2.5/8.2 6.5/2.96 95.082 Contour Method [42] 11.60 13.20 86.903 Exterior Contours and Shape Features[42] 06.90 06.50 93.804 Local Granulometric Size Distributions [47] 07.00 05.00 -5 Back-Propagation Neural Network Prototype [46] 10.00 06.00 -6 Geometric Centers [36] 09.00 14.58 -7 Two-stage neural network classifier [25] 03.00 09.81 80.818 Distance Statistics [40] 34.91 28.30 93.339 Modified Direction Feature [26] - - 91.12

10 Hidden Markov Model and Cross-Validation [11] 11.70 00.64 -11 Discrete Random Transform and a HMM [48] 10.00 20.00 -12 Kernel Principal Component Self-regression [23] 03.40 08.90 -13 Parameterized Hough Transform [49] - - 95.2414 Smoothness Index Based Approach [50] - - 79.0015 Geometric based on Fixed-Point Arithmetic [51] 4.9-15.5 5.61-16.39 -16 HMM and Graphometric Features [10] 23.00 01.00 -17 Virtual Support Vector Machine [52] 13.00 16.00 -18 Waveletbased Verification [53] 10.98 05.60 -19 Genetic Algorithm [44] 01.80 08.51 86.00

Performance Comparison

Sr. Approach FAR FRR EER Accuracy

1Signature Recognition -Clustering Technique 2.5/8.2 6.5/2.96 3.29/8.89 95.08

2 ER2 Dynamic Time Wrapping [30] - - 7.20 -3 On line SRS -Digitizer Tablet [24] 7.50-1.10 03.90 - -4 Image Invariants and Dynamic Features [54] - - - 83.005 On Line SRS Model Guided Segmentation [6] 0.80 - 3.406 Conjugate Gradient Neural Networks [55] - - - 98.407 Consistency Functions [56] 01.00 07.00 - -8 Variable Length Segmentation and HMM [58] 04.00 12.00 11.50 -9 Implementing a DSP Kernel [3] < 0.01 - - >99.0010 Dynamic Feature of Pressure [43] 6.80 10.80 - -11 Low cost Dynamic SRS [45] 7.00 6.00 - -

Performance Comparison with On Line & Hardware Based Signature Recognition Systems

ConclusionThe system uses conventional as well as non-conventional global features. These features

include Vector Quantization based codeword histogram, Walsh Coefficients, Grid & Texture Information Features, and Geometric Centers.

The Vector Quantization based codeword histogram has been proposed first time as a cluster feature for signature verification and it is effectively used for the purpose. This feature has Correct Classification Ratio (CCR) of 77.14%.

The other contributions include Walsh coefficients of the pixel distribution of the signatures. This feature has individual CCR of 59.17%.

Grid & Texture information features and successive geometric centers are the modified features that are used for signature recognition.

Signature verification as well as signature recognition is possible with the program developed.

Overall Accuracy of the system is 95.08%. The system has FAR of 2.5 % & FRR of 6.58 % in verification mode and FAR of 8.20 % and FRR of 2.96% in the recognition mode. For the FAR-FRR the equal rate EER is 3.29%

Paper PublishedNational Level technical papers:

1. New Parameter for Signature Recognition: Walsh Coefficient of Vertical and Horizontal Histogram, National Conference on Communication and Signal Processing (NCCSP 2007), Mumbai,April-2007

2. Signature Recognition by Novel and Simple Contour Technique, National Conference on Communication and Signal Processing (NCCSP 2007), Mumbai, April-2007

3. Successive Geometric Centers of a signature template, National Conference on Signal Processing & Automation (NCSPA 2007), Pune, September 2007

4. Grid & Texture Features for signature recognition, National Conference on Emerging Trends in Control & Instrumentation-(NCETCI 2007), Mumbai, October 2007

International Level technical papers:

1. Walsh Coefficients of the Horizontal & Vertical Pixel Distributions of Signature Template, International Conference of Information Processing 2007 (ICIP 2007), Bangalore, August 2007

2. Vector Quantization applied for Signature Recognition, International Conference on Advances in Computer Vision and Information Technology 2007 (ACVIT 2007), Aurangabad, Maharashtra, Nov 2007

3. Performance Analysis of Geometric centers of Depth2, Paper Selected for International Conference on Emerging Technologies and Applications in Engineering Technology and Sciences (ICETAETS 2008), Rajkot, January 2008

4. Performance Analysis of Grid & Texture Features, Paper Selected for International Conference on Sensors, Signal Processing, Communication, Control and Instrumentation (SSPCCIN-2008), Pune, January 2008

5. Performance Analysis of Codeword Histogram & Spatial Moments for Signature Recognition, Paper Selected for SPIT-IEEE Colloquium 2008, Mumbai, February 2008

References

file:///mnt/temp/unoconv/20150106061522/References.pdf

Questions ?

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