=Paper=
{{Paper
|id=None
|storemode=property
|title=Multi-script Off-line Signature Verification: A Two Stage Approach
|pdfUrl=https://ceur-ws.org/Vol-1022/Paper06.pdf
|volume=Vol-1022
|dblpUrl=https://dblp.org/rec/conf/icdar/PalPB13
}}
==Multi-script Off-line Signature Verification: A Two Stage Approach==
https://ceur-ws.org/Vol-1022/Paper06.pdf
Multi-script Off-line Signature Verification: A Two Stage Approach
Srikanta Pal Umapada Pal Michael Blumenstein
School of Information and Computer Vision and Pattern School of Information and
Communication Technology, Recognition Unit, Indian Statistical Communication Technology,
Griffith University, Gold Coast Institute, Kolkata, India, Griffith University, Gold Coast,
Australia, Email: Email: umapada@isical.ac.in Australia, Email:
srikanta.pal@griffithuni.edu.au m.blumenstein@griffith.edu.au
Abstract—Signature identification and verification are of great been devoted to the task of multi-script signature
importance in authentication systems. The purpose of this verification. Very few published papers involving multi-
paper is to introduce an experimental contribution in the script signatures, including non-English signatures, have
direction of multi-script off-line signature identification and been communicated in the field of signature verification.
verification using a novel technique involving off-line English,
Pal et al. [5] introduced a signature verification system
Hindi (Devnagari) and Bangla (Bengali) signatures. In the first
evaluation stage of the proposed signature verification employing Hindi Signatures. The direction of the paper was
technique, the performance of a multi-script off-line signature to present an investigation of the performance of a signature
verification system, considering a joint dataset of English, verification system involving Hindi off-line signatures. In
Hindi and Bangla signatures, was investigated. In the second that study, two important features such as: gradient feature,
stage of experimentation, multi-script signatures were Zernike moment feature and SVM classifiers were
identified based on the script type, and subsequently the employed. Encouraging results were obtained in this
verification task was explored separately for English, Hindi investigation. In a different contribution by Pal et al. [6], a
and Bangla signatures based on the identified script result. The multi-script off-line signature identification technique was
gradient and chain code features were employed, and Support
proposed. In that report, the signatures involving Bangla
Vector Machines (SVMs) along with the Modified Quadratic
Discriminate Function (MQDF) were considered in this (Bengali), Hindi (Devnagari) and English were considered
scheme. From the experimental results achieved, it is noted for the signature script identification process. A multi-script
that the verification accuracy obtained in the second stage of off-line signature identification and verification approach,
experiments (where a signature script identification method involving English and Hindi signatures, was presented by
was introduced) is better than the verification accuracy Pal et al. [7]. In that paper, the multi-script signatures were
produced following the first stage of experiments. identified first on the basis of signature script type, and
Experimental results indicated that an average error rate of afterward, verification experiments were conducted based
20.80% and 16.40% were obtained for two different types of on the identified script result.
verification experiments.
Development of a general multi-script signature
Keywords—Biometrics; off-line signature verification; multi-
script signature identification. verification system, which can verify signatures of all
scripts, is very complicated. The verification accuracy in
I. INTRODUCTION such multi-script signature environments will not be as
Biometrics are the most widely used approaches for successful when compared to single script signature
personal identification and verification. Among all of the verification [10]. To achieve the necessary accuracy for
biometric authentication systems, handwritten signatures, a multi-script signature verification, it is important to identify
pure behavioral biometric, have been accepted as an official signatures based on the type of script and then use an
means to verify personal identity for legal purposes on such individual single script signature verification system for the
documents as cheques, credit cards and wills [1]. identified script [10]. Based on this observation, in the
In general, automated signature verification is divided into proposed system, the signatures of three different scripts are
two broad categories: static (off-line) methods and dynamic separated to feed into the individual signature verification
(on-line) methods [2], depending on the mode of system. On the other hand to get a comparative idea, multi-
handwritten signature acquisition. If both the spatial as well script signature verification results on a joint English, Hindi
as temporal information regarding signatures are available and Bangla dataset, without using any script identification,
to the systems, verification is performed using on-line [3] is also investigated.
data. In the case where temporal information is not available The remainder of this paper is organized as follows. The
and the system can only utilize spatial information gleaned multi-script signature verification concept is described in
through scanned or even camera-captured documents, Section II. Section III introduces the notable properties of
verification is performed on off-line data [4]. Hindi and Bangla script. The Hindi, Bangla and English
Considerable research has previously been undertaken in signature database used for the current research is described
the area of signature verification, particularly involving in Section IV. Section V briefly presents the feature
single-script signatures. On the other hand, less attention has extraction techniques employed in this work. The classifier
details are described in Section VI. The experimental
settings are presented in Section VII. Results and a III. PROPERTIES OF HINDI AND BANGLA SCRIPT
discussion are provided in Section VIII. Finally, conclusions Most of the Indian scripts including Bangla and Devanagari
and future work are discussed in Section IX. have originated from ancient Brahmi script through various
II. MULTI-SCRIPT SIGNATURE VERIFICATION CONCEPT transformations and evolution [8]. Bangla and Devanagari
are the two most accepted scripts in India. In both scripts,
When a country deals with two or more scripts and the writing style is from left to right and there is no concept
languages for reading and writing purposes, it is known as a of upper/lower case. These scripts have a complex
multi-script and multi-lingual country. In India, there are composition of their constituent symbols. The scripts are
officially 23 (Indian constitution accepted) languages and 11 recognizable by a distinctive horizontal line called the ‘head
different scripts. line’ that runs along the top of full letters, and it links all the
In such a multi-script and multi-lingual country like letters together in a word. Both scripts have about fifty
India, languages are not only used for writing/reading basic characters including vowels and consonants.
purposes but also applied for reasons pertaining to signing
and signatures. In such an environment in India, the IV. DATABASE USED FOR EXPERIMENTATION
signatures of an individual with more than one language A. Hindi and Bangla Signature Database
(regional language and international language) are
essentially needed in official transactions (e.g. in passport As there has been no public signature corpus available for
Hindi and Bangla script, it was necessary to create a database
application forms, examination question papers, money
of Hindi and Bangla signatures. The Hindi and Bangla
order forms, bank account application forms etc.). To deal signature databases used for experimentation consisted of 50
with these situations, signature verification techniques sets per script type. Each set consists of 24 genuine
employing single-script signatures are not sufficient for signatures and 30 skilled forgeries. Some genuine signature
consideration. Therefore in a multi-script and multi-lingual samples of Hindi and Bangla, with their corresponding
scenario, signature verification methods considering more forgeries, are displayed in Table 1 and Table 2.
than one script are necessarily required.
Towards this direction of verification, the contribution of B. GPDS English Database
this paper is twofold: First, multi-script signature Another database, consisting of 50 sets from GPDS-160 [9],
verification considering joint datasets as shown in Figure 1, was also utilised for these experiments. Each signature set
the second is identification of signatures based on script, of this corpus consists of 24 genuine signatures and 30
and subsequent verification for English, Hindi and Bangla simple forgeries. The reason 50 sets were used from the
signatures based on the identified script result. A diagram of GPDS on this occasion, is due to the fact that the Bangla
this second verification mode is shown in Figure 2. and Hindi datasets described previously were comprised of
50 sets each, and it was considered important to have
Multi-script off-line Signatures (Signatures equivalent signature numbers for experimentation.
of English, Hindi and Bangla)
TABLE 1. SAMPLES OF HINDI GENUINE AND FORGED SIGNATURES
Verification based on Multi-script Signatures Genuine Signatures Forged signatures
Accuracy of Verification
Figure 1. Diagram of signature verification considering a joint dataset.
Multi-script Signatures
(English, Hindi and Bangla)
TABLE 2. SAMPLES OF BANGLA GENUINE AND FORGED SIGNATURES
Genuine Signatures Forged signatures
Signature Script Identification
Signatures of Signatures of Signatures of
English Script Hindi Script Bangla Script
English Hindi Bangla
Signature
V. FEATURE EXTRACTION
Signature Signature
Verification Verification Verification Feature extraction is a crucial step in any pattern
Figure 2. Diagram of multi-script signature identification recognition system. Two different types of feature
and verification based on English, Hindi and Bangla signatures. extraction techniques such as: gradient feature extraction
and the chain code feature are considered here.
A. Computation of 576-dimensional gradient Features f ( x) j x j x b
576-dimensional gradient features were extracted for this j
research and experimentation, which are described in paper where {xj} are the set of support vectors and the parameters
[7]. j and b have been determined by solving a quadratic
B. 64-Dimensional Chain Code Feature Extraction problem [11]. The linear SVM can be extended to various
non-linear variants; details can be found in [11, 12]. In these
The 64-dimensional Chain Code feature is determined as proposed experiments, the Gaussian kernel SVM
follows. In order to compute the contour points of a two- outperformed other non-linear SVM kernels, hence
tone image, a 3 x 3 window is considered surrounding the identification/verification results based on the Gaussian
object point. If any one of the four neighbouring points (as kernel are reported only.
shown in Fig. 3 (a)) is a background point, then this object
point (P) is considered as a contour point. Otherwise it is a B. MQDF Classifier
non-contour point. The Modified Quadratic Discriminant Function is defined as
The bounding box (minimum rectangle containing the follows [13].
character) of an input character is then divided into 7 x 7
blocks. In each of these blocks, the direction chain code for ( ) ( ) [ [‖ ‖ ∑ ]]
each contour point is noted and the frequency of the
direction codes is computed. Here, the chain code of four
directions only [directions 1 (horizontal), 2 (45 degree ∑ ( )
slanted), 3 (vertical) and 4 (135 degree slanted)] is used.
where X is the feature vector of an input character; M is a
Four chain code directions are shown in Fig. 3 (b). It is
mean vector of samples; is the ith eigen vector of the
assumed that the chain code of directions 1 and 5, 2 and 6, 3
sample covariance matrix; is the ith eigen value of the
and 7, 4 and 8, are the same. Thus, in each block, an array is
sample covariance matrix; k is the number of eigen values
obtained of four integer values representing the frequencies,
considered here; n is the feature size; is the initial
and those frequency values are used as features. Thus, for 7
estimation of a variance; N is the number of learning
x 7 blocks, 7 x 7 x 4= 196 features are obtained. To reduce
samples; and N0 is a confidence constant for s and N0 is
the feature dimensions, after the histogram calculation into 7
considered as 3N/7 for experimentation. All the eigen values
x 7 blocks, the blocks are down-sampled with a Gaussian
and their respective eigen vectors are not used for
filter into 4 x 4 blocks. As a result, 4 x 4 x 4 = 64 features
classification. Here, the eigen values are stored in
are obtained for recognition. To normalize the features, a
descending order and the first 60 (k=60) eigen values and
maximum value of the histograms from all the blocks, is
their respective eigen vectors are used for classification.
computed. Each of the above features is divided by this
Compromising on trade-off between accuracy and
maximum value to obtain the feature values between 0 and
computation time, k was determined as 60.
1.
VII. EXPERIMENTAL SETTINGS
A. Settings for Verification used in 1st Stage of Experiments
The skilled forgeries were not considered for training
purposes. For experimentation, random signatures were
(a) (b) considered for training purposes. For each signature set, an
Figure 3. Eight neighbours (a) For a point P and its neighbours (b) For a
point P the direction codes for its eight neighbouring points.
SVM was trained with 12 randomly chosen genuine
signatures. The negative samples for training (random
VI. CLASSIFIER DETAILS signatures) were the genuine signatures of 149 other
Based on these features, Support Vector Machines signature sets. Two signatures were taken from each set. In
(SVMs) and the Modified Quadratic Discriminant Function total, there were 149x2=298 random signatures employed
(MQDF) are applied as the classifiers for the experiments. for training. For testing, the remaining 12 genuine
signatures and 30 skilled forgeries of the signature set being
A. SVM Classifier considered were employed. The number of samples for
For this experiment, a Support Vector Machine (SVM) training and testing for these experiments are shown in
classifier is used. The SVM is originally defined for two- Table 3.
class problems and it looks for the optimal hyper plane, Table 3. No. of Signatures used per set in 1st Phase of Verification
which maximizes the distance and the margin, between the Genuine Random Skilled
nearest examples of both classes, named support vectors Signature Signatures Forgeries
(SVs). Given a training database of M data: {xm| m=1,..., M}, Training 12 298 n/a
the linear SVM classifier is then defined as: Testing 12 n/a 30
B. Settings for Verification used in 2nd Stage of Experiments identification stage by using the SVM classifier. The
1) Settings for Signature Script Identification accuracy of Bangla, English and Hindi are 85.19, 95.74 and
150 sets of signatures (50 sets of English, 50 sets of Hindi 98.33% respectively. Confusion matrices obtained using
and 50 sets of Bangla) were used for signature script SVM classifiers, and the 64-dimensional chain code features
identification. 30 sets of signatures from each script were investigated, are shown in Table 6.
considered for training, and the remaining 20 sets were TABLE 5. ACCURACY OBTAINED USING SVM AND MQDF CLASSIFIERS
considered for testing purposes. The number of samples for
Classifiers Identification Accuracy (%)
training and testing used in experimentation of the
identification approach are shown in Table 4. SVMs 93.08
MQDF 82.45
TABLE 4. SIGNATURE SAMPLES USED FOR SCRIPT IDENTIFICATION PHASE.
English Signatures Hindi Signatures Bangla Signatures TABLE. 6. CONFUSION MATRIX OBTAINED USING THE CHAIN CODE
FEATURE AND SVM CLASSIFIER
Genuine Forged Genuine Forged Genuine Forged
Bangla English Hindi
Training 720 900 720 900 720 900
Bangla 920 19 141
Testing 480 600 480 600 480 600 English 27 1034 19
Hindi 10 8 1062
2) Settings for Signature Verification after Signature Script
Identification Based on the outcomes of the identification phase,
The verification task in the second stage was explored verification experiments subsequently followed.
separately for English signatures, Hindi signatures and Verification results obtained for individual scripts were
Bangla signatures based on the identified script result. calculated on 93.08% (identification rate) accuracy levels.
Signature samples (30 sets from each script) that were In this phase of experimentation, the SVMs produced an
considered for training purposes in signature script overall AER of 21.10%, 13.05% and 15.05% using English,
identification were not used for the individual verification Hindi and Bangla signatures respectively. The overall
task. Only the correctly identified samples from 20 sets verification accuracy obtained for the second major
(used for the testing part in identification) were considered experiments (identification plus verification) was 83.60%
for verification. For each signature set, an SVM was trained (average of 78.90% of English, 86.95% of Hindi and
with 12 genuine signatures. The negative samples for 84.94% of Bangla).
training were 95 (19x5) genuine signatures of 19 other
signature sets. B. Comparision of Performance
VIII. RESULTS AND DISCUSSION From the experimental results obtained, it was observed that
the performance of signature verification in the second set
A. Experimental Results
of experiments (identification and verification) was
1) First Verification Experiments encouraging compared to the signature verification accuracy
In this stage of experimentation, 8100 (150x54) signatures from the first experiment set (verification only). Table 7
involving English, Hindi and Bangla scripts were employed demonstrates the accuracies attained in the first experiment
for training and testing purposes. At this operational point, set as well as separate verification results for English, Hindi
the SVMs produced an AER of 20.80%, and an encouraging and Bangla from the second experiment set.
accuracy of 79.20% was achieved in this first mode of
verification. TABLE 7. VERIFICATION ACCURACIES RESULTING FROM DIFFERENT
EXPERIMENTS
2) Second Verification Experiments
In this stage of verification the signatures are identified Verification Techniques Accuracy (%)
based on their script and subsequently, the identified Experiment Sets Dataset Used
signatures are applied separately for verification. In the English, Hindi and
signature script identification stage, only 64-dimensional 1st experiment 79.20
Bangla
chain code features were used because a slightly better
accuracy was obtained when compared to the gradient English 78.90
feature. The MQDF classifier was also taken into account in nd
2 experiment Hindi 86.95
the script identification step applying chain code features for Bangla 84.94
a better accuracy, but MQDF did not achieve the better
result as compared to SVMs in this study. To get a
comparative idea, script identification results using two In the second stage of verification, the overall accuracy is
different classifiers with chain code features are shown in 83.60% (Avg. of 78.90%, 86.95% and 84.94%) which is
Table 5. An accuracy of 93.08% is achieved at the script 4.40 (83.60-79.20) higher than the accuracy in the first
stage. The comparison of these two accuracies is shown in substantially affects the verification accuracy, indicates an
Table. 8. important step in the process. The comparatively higher
verification accuracy obtained in the second experimental
TABLE 8. ACCURACY IN DIFFERENT PHASES OF VERIFICATION
approach is likewise a substantial contribution. The gradient
Verification Experiment Verification Accuracy (%) feature, chain code feature as well as SVM and MQDF
Without Script Identification 79.20 classifiers were employed for experimentation. The idea of a
With Script Identification 83.60 multi-script signature verification approach, which deals
with an identification phase, is a very important contribution
to the area of signature verification. The proposed off-line
From the above table it is evident that verification accuracy multi-script signature verification scheme is a new
with script identification is much higher than without script investigation in the field of off-line signature verification. In
identification. This increased accuracy is achieved because the near future, we plan to extend our work considering
of the proper application of the identification stage. This further sets of signature samples, which may include
research clearly demonstrates the importance of using different languages/scripts.
identification in multi-script signature verification
techniques. X. ACKNOWLEDGMENTS
C. Error Analysis Thanks to my colleague Mr. Nabin Sharma for his help
Most of the methods used for signature verification generate towards the preparation of this paper.
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