CLASSIFIER PERFORMANCE EVALUATION FOR OFFLINE SIGNATURE VERIFICATION USING LOCAL BINARY PATTERNS Rameez Wajid
Atif Bin Mansoor
Institute of Avionics & Aeronautics, Air University Islamabad, Pakistan
[email protected]
Institute of Avionics & Aeronautics, Air University Islamabad, Pakistan
[email protected]
ABSTRACT
and implement. Offline signatures, as biometric have spanned over many decades and also resulted in growth of expert forgers. Thus there is a great need for a reliable Offline Signature verification system for authorization.
Handwritten signatures are one of the most widely used biometrics, particularly in financial and legal transactions. Offline Signature verification is still one of the most challenging problems in biometrics. In this study, we have evaluated the performance of different classifiers for offline signature verification based upon local binary patterns feature set. The feature vector is formed by dividing the signature images into twelve local regions and forming a code matrix by their LBPs. The histogram of each code matrix is formulated and concatenated. The dimensionality of feature vector is subsequently reduced by keeping the 256 DCT coefficients of the concatenated vector. We have investigated the performance of seven classifiers on FUM-PHSDB dataset comprising 20 classes of genuine and forged signatures of depth 20 and 10 respectively. Experimental findings depict that LS-SVM performs the best among the seven classifiers, achieving the Equal Error Rate (EER) of 13%. Index Terms— Offline signature verification, local binary patterns, classification, LS-SVM.
1.
INTRODUCTION
The authorization of documents is an important aspect of financial and legal transactions. The handwritten signatures are still the most common used biometrics to authorize such documents. Although other biometrics, such as fingerprint and iris verification, are being used as criteria for personal identification in online financial transactions, signatures are still the backbone of the paper based transactions in various sectors. Further personnel identification mechanisms, particularly in developing countries still employ handwritten signature as the most discriminating identifier. The handwritten signatures are broadly classified as either online or offline [1]. The online signatures are captured in real-time, using specialized devices. In this way the dynamic information such as the altitude, azimuth, pressure and velocity attributes of the signature are available for the verification process. In contrast, the offline signatures are collected as images, without availability of any dynamic information. Thus offline signature verification is considerably challenging to design
ISBN: 978-82-93269-13-7 ©2013 University of Paris 13
2.
LITERATURE REVIEW
Various techniques have been employed for handwritten signature verification. In [2], Ferrer et al. present a structure based approach for offline signatures in which stroke distributions in polar and Cartesian coordinates have been used as features. The features are tested with different classifiers such as Hidden Markov Model (HMM), SVM and Euclidean distance. The HMM showed the best results among the tested classifiers. Sabourin et al. used granulometric size distributions as local shape descriptor for offline signature verification [3]. A database of 800 signatures is used in the experiment, encompassing signatures from different languages. The nearest neighbor and threshold classifiers demonstrate promising results. In [4], Ferrer et al. investigated the robustness of local binary patterns against rotation invariant features for signature verification. The Support Vector Machine (SVM) is employed as classifier with different kernels. The signatures are taken from various databases, like GPDS960Gray signatures and MCYT offline signatures, and blended in contrasting backgrounds to account for robustness in real-life applications. The results show the local binary patterns are more robust than rotation invariant features for offline signature verification. Tselios et al. used a grid-based feature for verification in [5]. The relative pixel distribution is statistically exploited along pre-determined paths using Chebyshev distances. The classification is done using a combination of similarity based classifier and SVM. Ghandali and Moghaddam [6] specifically conducted experiments on signatures of Persian origin. Discrete wavelet transform (DWT) is used for analysis of signature images. The signatures are decomposed and feature vectors are calculated by merging the high-frequency decomposition coefficients in sub-bands. Subsequently the Euclidean distance classifier is used for signature verification.
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Vargas et al. attempted verificatioon using wavelet analyses in [7]. The gray-level variattions of signature strokes are measured by wavelet decompposition analysis in combination with statistical texture features. The SVM classifier using RBF kernel is utilized too assess the feature vector results. In [8] Contourlet Transforms is used for verification of offline signatures. The signature images are decomposed at various directional and resolution levels l using the Contourlet transform, and the formed feature vector is subsequently used for classification by Euclidean E distance classifier. Our work is based on the use of grray level features, namely, local binary patterns in an approoach similar to [4]. Seven different classifiers are compareed with respect to their classification accuracy. The restt of the paper is organized as follows: Section 3 presents the experimentation including discussions on the signature database, the features extraction and the classifiers c used for performance evaluation. The experimeental results along with the comparative analysis are incluuded in Section 4. The paper is concluded in Section 5.
3. 3.1.
E Gray Level Feature Extraction
3.2.
Local binary pattern (LBP) is an efficient gray-level feature commonly used in textture classification [10]. The popularity of the LBP operattor, especially in real-time applications, is mainly due to its simple computation and its robustness to illuminatiion variations and other monotonic gray-scale changes. The original LBP operator works by thresholding the neighborhood of different sizees [10]. This is achieved by comparing the center value pixxel to each neighbor, giving a binary outcome as result. Heence the value of a pixel, v, is calculated by:
v
,
v
v 2
(1)
where, v is the value of original o pixel, v the value of neighbor, P is the number of o neighbors and R is radius over which LBP is applied, andd is a step function defined as:
1, 0,
0
(2)
ON EXPERIMENTATIO
The database
The FUM-Persian Handwritten Signaturee Database (FUMPHSDB 2006) [9] is used in experimeents. The database contains 20 signature classes, consistinng of 20 genuine signatures and 10 expert forgery siggnatures for each individual. The signatures were obtaineed by signing on a 10×10 cm white paper using a black penn and later scanned at a resolution of 300 DPI (dot per innch). Fig. 1 shows genuine and forged signature samples from fr a class of the database.
Ojala et al. further observved frequent appearance of specific LBPs in certain regionns in an image, resulting in definition of a uniform rotation invariant LBP:
,
v
v
v
v
v
v
(3)
where, the superscript, , represents r uniform LBP and is the value of adjacent neighbor n to v .
The gray-level image froom a signature class was divided into 12 equal blocks as a described by Ferrer et al for feature extraction [4]. The selected s blocks were four in vertical and three in horizonntal direction, with a 60% overlap. We have applied thee rotation invariant LBP to these blocks forming a code maatrix. The histogram of each code matrix is further formulaated, having a dimension of 255. Fig. 2 shows the histogram ms of code matrices of two blocks, clearly demonstrating thheir variations.
Fig.1 Genuine (left) and forged (right) siggnature sample from FUM-PHSDB
We divided the database in two equall sets. The training and validation set contains 10 originall signatures and 5 skilled forgeries for each class. Thuss, a total of 300 samples are used for training the classiifier and a further 300 for verification.
Fig.2 Histograms of a code maatrix of individual blocks
Subsequently, the 12 histoggrams were concatenated to form the feature vector of o 3060 dimension. The dimensionality of feature veector was reduced by the
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Discrete Cosine Transform (DCT). calculated as follows:
The DCT was k = 1,2,…N
(4)
Where, is the nth value of LBP histogram, N is the dimension of input vector (3060 in this case) and, 1
√
2
(5)
The first 256 coefficients of the DCT form the final feature vector for classification. The histogram of a reduced feature vector is shown in Fig.3.
compared against all other samples of different classes to assess its validity as a genuine sample. 3.3.3 Distance Likelihood Ratio Test The Distance Likelihood Ratio Test (DLRT) compares two classes or models using statistical analysis. The likelihood between two competing models, termed as null and alternative model, is calculated by their log-likelihood parameter. The decision boundaries can be determined if the distribution of the likelihood ratio is known. It’s made simpler using Wilks’ theorem which enables approximation of the distribution as being chi-squared for large sample size. In the experiment, Euclidean distance has been used for computation the distance between samples and class centers. 3.3.4 Artificial Neural Networks The Artificial Neural Network (ANN) classifier is an approach modeled on the biological neural networks. It uses similar structure to the neurons by interconnecting several nodes. The ANN is usually an adaptive classifier as its structure changes during its learning process. There are three main layers in the classifier, namely, the input layer, hidden layer and the output layer. The complexity of the system depends on the number of elements in the hidden layer. For this experiment, 20 layers are used in the hidden element. 3.3.5 Fisher's linear discriminant
Fig.3 Histogram of reduced LBP vector
3.3.
Classifiers
The Fisher's linear discriminant (FLD) classifier is based on formulating a linear combination of features for calculating class boundaries [12]. The classification is reliant on the means and variances of the relevant classes. The separation between the classes is defined as the ratio of the variances between them to the variances within the classes.
3.3.1 Support Vector Machines
3.3.6 Logistic Discriminant
The Support Vector Machines (SVM) is an important classifier which is increasingly used in signature verification systems [1]. It is a supervised learning mechanism which makes use of the implicit mapping technique, resulting in a high dimension feature space for classification. The ‘kernel trick’ on high dimension feature space is applied to get a kernel function and then the classification boundaries are calculated by avoiding carrying out cumbersome calculations on the feature space itself [11]. For this experiment, the Radial Basis Function (RBF) kernel is employed with a slack variable weight of 1 and tolerance value 0.00001.
Logistic regression analysis is a simple model for binary classification. It has the ability to predict the outcome of a dependent variable which can be related to different categories. A logistic function is utilized to model the probabilities of possible outcomes. The model used by logistic regression differs from linear regression in linking of the outcomes to the predictor function. The probabilities and regression coefficients are usually determined using maximum likelihood estimation or other optimization procedure. The closed form solution for a generalized linear model is difficult to obtain, hence, iterative methods like Iteratively Reweighted Least Squares (IRLS). For this experiment, maximum of 500 IRLS iterations are used with a step size of 0.05.
3.3.2 Least Squares-Support Vector Machines The Least Squares version of SVM (LS-SVM) is a kernelbased learning method. It differs from the classical SVMs as it substitutes the complex quadratic programming by employing the solution of an indefinite set of linear equations [11]. We tested different kernels like poly, linear wav and RBF. Finally, the RBF kernel is used in this experimentation due to its best results. The calculation of class boundaries is usually done by ‘one-to-one’ or ‘oneto-all’ approaches. In this study the ‘one-to-all’ approach is used to tune the classifier i.e. a signature sample is
3.3.7 Naive Bayes The Naive Bayes classifier, as the name suggests, is a simple implementation of Bayes theory of probability. The probabilistic model is based on the assumption that all the features are conditionally independent of each other. This assumption though false, generally works well in classification problems [12].
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For this experiment, independent random variables were taken as the base random variable for classifier computations.
4. 4.1.
RESULTS
Table I clearly display the suitability of SVM based classification methods over others for offline signature verification. The best results are obtained using the LSSVM classifier, achieving an EER of 13%. 4.2.
Experimental Results
The experiments have been implemented in MATLAB environment on 4 GB RAM, 2.50 GHz Intel Core i5 processor.
Comparative Analysis
The EER of LS-SVM classifier is compared with different approaches reported in literature in Table II. The comparative analysis shows promising results by employing LS-SVM based classification using LBP features. PERFORMANCE COMPARISON
TABLE II.
Fig.4 Receiver Operating Characteristic (ROC) Curves for different classifiers
The Receiver Operating Characteristic (ROC) curves obtained from the classifications are displayed in Fig 4. The classifier performance is evaluated in Table I, using Equal Error Rate (EER) as the performance parameter for each classifier. TABLE I.
CLASSIFIER PERFORMANCE EVALUATION Classifier Performance Evaluation Classifier EER
Rank 1
LS-SVM
13 %
2
SVM
22 %
3
DLRT
24 %
4
ANN
32 %
5
FLD
36 %
6
Log Discriminant
36 %
7
Naive Bayes
47 %
S No
Performance Comparison Technique / Feature Classifier Performance
1
Stroke distributions [2]
SVM
EER = 13.12%
2
Gray level features with Discrete Cosine Transform [4]
LS-SVM
EER = 11.05 %
3
Pixel distributions using Chebyshev distances. [5]
Combinati on of similarity based classifier and SVM
EER = 7%
4
Discrete Wavelet Transform and Image Fusion [6]
Difference based Classifier
FRR = 8.9%
5
Gray-Level Stoke Variations [7]
SVM
EER = 14.32%
6
Contourlet Transform [8]
Difference based Classifier
EER = 14.00%
7
PROPOSED LBP
LS-SVM
EER = 13%
5.
CONCLUSION
FAR = 10%
The paper presents a comparative analysis of seven classifiers for offline signature verification using gray level features based upon local binary patterns. SVM based classifiers outperform the others, whereby LS-SVM demonstrated the best classification accuracy with EER of 13%. The results were also compared with different published approaches, depicting promising result of the proposed approach. In future, we intend to extend the approach to multi-modal offline signature verification.
6.
REFERENCES
[1] D Impedovo, G Pirlo, “Automatic Signature Verification: The State of the Art”, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 38, No. 5, September 2008, pp. 609 – 635.
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[2] M. A. Ferrer, J B. Alonso, C M. Travieso, “Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 6, June 2005, pp. 993 – 997. [3]
R Sabourin, , G Genest, F J. Prêteux, “Off-Line Signature Verification by Local Granulometric Size Distributions”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 9, September 1997, pp. 976 – 988.
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Pourshahabi, M.R.; Sigari, M.H.; Pourreza, H.R., “Offline Handwritten Signature Identification and Verification Using Contourlet Transform”, International Conference of Soft Computing and Pattern Recognition, 2009, pp. 670 – 673.
[9]
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