International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN

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1 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN REDUCING FALSE ACCEPTANCE RATE IN OFFLINE WRITER INDEPENDENT SIGNATURE VERIFICATION SYSTEM THROUGH ENSEMBLE OF CLASSIFIERS Ashok Kumar 1, Karamjit Bhatia 2 1 Research Scholar, Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar & Associate Professor, Department of Computer Applications, Invertis University, Bareilly, India 2 Professor, Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India ABSTRACT: Handwritten signature verification is a very challenging and critical task. This work aims at proposing an efficient offline handwritten signature verification model using writer independent approach. The prime focus of this work is on reducing the false acceptance rate of genuine signatures of writers while letting false rejection rate at a satisfactory level through ensemble of classifiers. The k-fold cross validation technique is used to develop ensemble of classifiers. The performance of the ensemble of classifiers, the support vector machine with polynomial kernel, is analyzed using the signature database of writers. The efficacy of geometric and uniform rotation invariant local binary pattern features is investigated to build a reliable writer independent offline handwritten system. The experiments exhibit 0.00 %, 0.00 % and 1.00 % false acceptance rate for random, simple and skilled forgeries, respectively while allowing false rejection rate 5.00 %. Keywords: Writer Independent Offline Handwritten Signature Verification System, Geometric Features, Uniform Rotation Invariant Local Binary Pattern Descriptor, Support Vector Machine, Ensemble of Classifiers, False Acceptance Rate, False Rejection Rate [1] INTRODUCTION The signature of a person has tremendous significance as it is used not only for the identification of a person but also for establishing the authenticity of official documents. A Handwritten Signature Verification (HSV) system authenticates the signature of a person as genuine or forges. In previous few decades, several offline and online signature verification Ashok Kumar and Karamjit Bhatia 1

2 REDUCING FALSE ACCEPTANCE RATE IN OFFLINE WRITER INDEPENDENT SIGNATURE VERIFICATION SYSTEM THROUGH ENSEMBLE OF CLASSIFIERS systems have been proposed [1]. In online approach, optical pen is used by writer for signature and sensors are used to pick dynamic characteristics of handwriting like speed of writing, order of strokes, and pressure at various positions of the signature etc. On the other hand, in offline approach, white paper sheet is used to collect the signatures of writers and then optical scanner is used to convert these signatures into digital form. Due to unavailability of dynamic information, the development of an efficient and reliable offline signature verification system is supposed to be a tough task as compared to the development of its counterpart. Because of same reason, it is observed that the online signature verification systems outperform the offline signature verification systems. For developing a signature verification system, a forgery set is used which is divided into random, simple, and skilled forgeries subsets. The random forgery is a genuine signature sample of another writer. In simple (also known as unskilled) forgery the forger knows only the genuine writer s name whereas in case of skilled forgery (also known as simulated forgery) the forger knows genuine signature sample of writer very well and has practiced it many times [2]. The random, simple and skilled forgeries of genuine signature sample of a writer are shown in [Figure-1]. To measure the efficiency of HSV systems, two performance metrics are used by researchers namely- False Rejection Rate (FRR) and False Acceptance Rate (FAR). The False Rejection Rate (also known as Type I error) is the percentage of genuine signatures of writer rejected as forgery signature whereas the False Acceptance Rate (also known as Type II error) is the percentage of forgery signatures of writer accepted as genuine signature. Many researchers have reported the performance of a HSV system in terms of Average Error Rate (AER), sometimes called Mean Error Rate (MER), which is the average of FRR and FAR. Figure: 1. Genuine and forgery signatures Writer dependent (WD) and Writer independent (WI) are two approaches to develop an offline signature verification systems [3]. In WD approach, an individual model is built for every writer on the basis of two dissimilar pattern classes, C1 and C2, where C1 is a set of a specific writer genuine signature samples and class C2 contains the forgery signature samples. The writer dependent approach suffers from major problems- the requirement of the vast number of genuine signature samples and incapability to absorb new writer without generating new personal model. On the other hand, writer independent approach, also called global model, requires only one model to deal with all writers and is capable to absorb unknown writer without retraining the model. This global model approach perceives the signature verification problem as a two-class problem, where one class G1 contains genuine samples of all writers and other class G2 contains forgeries [Figure-2]. The improvement of 2

3 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN WI approach over WD is that one can build reliable model even when a few number of genuine signature samples are available. In writer independent HSV system, one of the prime challenge is reducing the FAR for random, simple and simulated forgeries as much as possible while keeping FRR at acceptable level. The present work aims at developing an offline writer independent handwritten signature verification system with major focus on reducing the false acceptance rate. For this work, a signature database of 260 writers is created. Two feature sets, geometric features and uniform rotation invariant local binary pattern features, are extracted from the signature samples for the performance evaluation of the ensemble of classifiers, the Support Vector Machine with Polynomial Kernel (SVM Poly). The classifiers of ensemble are trained with local and global geometric and uniform rotation invariant local binary pattern features using only genuine signature samples and random forgeries. Simple and simulated forgeries of genuine signature are not used in training process but used in the testing phase only. The prime aim of these classifiers is to classifying the handwritten signature as genuine or forge. The performance of proposed approach is evaluated and compared with existing approaches for writer independent signature verification system. Figure: 2. Global model signatures area for different authors The rest of the paper is intended as: Section 2 presents the literature survey related to writer independent offline HSV system. Section 3 describes the functioning of WI approach. Section 4 presents the detail of proposed method. Section 5 discusses results of experiments and finally the conclusion of this paper is given in section 6. [2] LITERATURE SURVEY Writer independent approach for HSV system is not a widely addressed research problem as compared to its counterpart, the writer dependent approach. This approach was initially proposed by Cesar Santos et al. [4]. Authors claimed AER 8.02% by using graphometric features and Neural Network (NN) classifier for their approach. To improve the performance of writer independent HSV system, D. Bertolini et al. [2] used ensemble of Support Vector Machine (SVM) classifiers and obtained AER 6.28% using graphometric features. Directional Probability Density Function (DPDF) and Extended Shadow Code Ashok Kumar and Karamjit Bhatia 3

4 REDUCING FALSE ACCEPTANCE RATE IN OFFLINE WRITER INDEPENDENT SIGNATURE VERIFICATION SYSTEM THROUGH ENSEMBLE OF CLASSIFIERS (ESC) grid based techniques to extract the information about the orientation of stroke and spatial distribution from signature image are used by D. Rivard et al. [5]. Authors claimed AER 5.19 % by using SVM classifier. George S. Eskander et al. [6] proposed a hybrid WI- WD system using SVM classifier to classify the signature as genuine or forge. Spatial distribution and orientation of stroke features are used in their approach and obtained average error rate 5.38%. R. Kumar et al. [7] examined SVM and NN using surroundedness property based features of a signature. Authors achieved accuracy of 86.24% for GPDS300 database. J. Swanepoel et al. [8] developed a better writer independent HSV than the existing systems by using discrete radon transform (DRT) and dynamic time warping (DTW) to extract the features from signature image and obtained AER 4.93%. S. George et al. [9] claimed the development of a less complex, more accurate and more secure WI-WD offline HSV system. Authors reported AER 7.24 % by using SVM classifier. A writer independent HSV system using reduced number of references and feature dissimilarity measures thresholding for classification has been proposed by A. Hamadene et al. [10]. Authors obtained 18.42% AER by using one class SVM classifier and Directional Code Co-occurrence Matrix (DCCM) and Contourlet Transform (CT) features. Luiz G. Hafemann [11] used Deep Convolutional Neural Networks (CNN) to learn features in their approach and obtained 3.96% average error rate using SVM RBF classifier. [3] WRITER INDEPENDENT APPROACH In writer independent approach, reference signature samples Ref k (k = 1, 2,, n) are compared with questioned signature sample Q to classify the questioned signature sample as genuine or forge. Extracted features from reference signature samples and questioned signature sample are used to form the dissimilarity feature vectors. E. Pekalska et. al. [12] introduced the concept of dissimilarity representation and the idea was that dissimilarities were supposed to be large for the objects belonging to different classes and small for objects of same class. To form dissimilarity feature vector def i = Ref i Q, the difference between feature vector of reference sample and feature vector of questioned sample is computed and is fed to classifier to take the partial decision. Finally, fusion strategies are used to obtain final decision from partial decisions. Writer independent approach is represented in [Figure-3]. [4] PROPOSED METHOD The major steps in the proposed method to develop a writer independent offline handwritten signature verification system include: creation of signature database, feature extraction from preprocessed signature images, creation of ensemble of classifiers and classification of the questioned signature images as forge or genuine using ensemble of classifiers. [4.1] SIGNATURE DATABASE In the present work, signature database of 260 writer s genuine, simple forgery and skilled forgery signature samples is used. The ensemble of classifiers are trained using signature samples of 160 writers and remaining 100 writers signature samples are used to test the performance of ensemble. To collect the signature samples of writers, A4 white paper 4

5 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN sheet is used and signature samples are collected from undergraduate and postgraduate students in two different sessions, once in fifteen days during one month and then scanned by a scanner at 600 dpi gray level to convert the signature samples into digital form. In each session, each writer is signed 10 genuine signatures. Four different students for each genuine writer are selected for making forgeries and each forger signed 5 signatures for simple forgeries and 5 signatures for simulated forgeries for assigned writer, i.e. total 20 simple forgeries and 20 simulated forgeries are collected per genuine writer. To produce simple forgeries the forger knew only the name of the writer whereas forgers practiced many times with genuine signatures of assigned writer to produce simulated forgeries. The dissimilarity-based approach is adopted in this study and the classifiers are trained with positive (genuine) and negative (forgery) samples. Positive samples are generated by computing the dissimilarity vectors among 6 genuine samples per writer, thus 15 distinct combinations are obtained. In this way, total 2400 positive samples are generated from 160 writers. To form the negative samples, dissimilarity vectors are computed from 4 genuine samples of the 5 writers and 4 genuine samples of randomly selected 140 writers from remaining training set. This resulted into 2800 negative samples. Thus, to train ensemble of SVM Poly classifiers total 5200 (2400 positive samples plus 2800 negative samples) dissimilarity vectors are used. The number of required genuine and forgery samples of signature is dependent on the number of references are used for questioned signature in the testing process. In present approach, forgery and genuine signature samples of those writers are used for testing which are not considered for the training process. Figure: 3. Writer independent approach for offline handwritten signature verification system. [4.2] FEATURE EXTRACTION Geometric and uniform rotation invariant local binary pattern feature vectors are used in the present work for developing writer independent HSV system. To extract the features, Ashok Kumar and Karamjit Bhatia 5

6 REDUCING FALSE ACCEPTANCE RATE IN OFFLINE WRITER INDEPENDENT SIGNATURE VERIFICATION SYSTEM THROUGH ENSEMBLE OF CLASSIFIERS preprocessed signature images are used. To remove the noise from signature image, median filter is used in preprocessing phase. After this, gray level signature image is transformed into binary image by calculating threshold value using Otsu s method [13]. The signature image is then cropped and resized to the image size 256 x 512. [4.2.1] GEOMETRIC FEATURES In the present study, ten features namely- signature area, mean, standard deviation, number of connected components, perimeter of signature image, number of horizontal edges, number of vertical edges, number of edge points, number of lines (horizontal and vertical), and number of branch points are adopted to constitute geometric feature vector. To extract the geometric feature vector from preprocessed signature image of size 256 x 512, following steps are performed: 1. Extracted ten features from whole signature image as global features. 2. The signature image is divided into four equal parts and again same ten features from each part of the signature image are extracted to get local features. Thus, in all 10 global and 40 local features are extracted from signature image to form geometric feature vector of length 50. [4.2.2] UNIFORM ROTATION INVARIANT LOCAL BINARY PATTERN FEATURES In the basic Local Binary Pattern (LBP), a binary code is generated corresponding to each pixel position in the image. The binary code is generated by using center pixel and other pixels in the neighborhood. Let circular neighborhood (A, B) where A denotes the number of sampling points and B denotes the radius of the neighborhood [14]. The coordinate value of neighborhood pixels, denoted as (m r, n r ), where r = 1, 2,, A, around pixel position (m, n) are obtained by the means of equation (1) and interpolation is used to convert the coordinate value into integer coordinates [15]. The label of LBP for the central pixel (m, n) of signature image IMG is obtained using equation (2). Equation (3) is used to find the value of threshold function th(z) used in equation (2). (1) (2) A local binary pattern which contains at most two transitions from binary bit 0 to 1 or binary 1 to 0 is known as uniform LBP (extension of LBP) [16]. For example, the patterns , and are uniform patterns whereas and are not uniform patterns. All non uniform LBP are assigned to a single histogram bin and a separate histogram bin is assigned to every uniform LBP in the histogram computation of (3) 6

7 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN uniform LBP. Rotation invariant LBP operator finds the bit pattern of minimum value from all circularly rotated binary bit patterns. For example, the bit patterns , and diverse rotations of the same local pattern and all of these correspond to normalized bit pattern In nutshell, uniform rotation invariant LBP feature descriptor of length 256 is obtained by performing above procedure using 3 x 3 neighborhoods. [4.3] CREATION OF ENSEMBLE OF CLASSIFIERS Ensemble of classifiers is created using k-fold cross validation method [17]. The value of k is taken to be 25 to obtain 25 training sets by dividing the original training set into 25 partitions randomly. Support Vector Machine with Polynomial Kernel (SVM Poly) classifiers are trained using obtained training sets. In this way, 25 diverse SVM Poly classifiers are generated and all these classifiers are used to create the ensemble of 25 SVM Poly classifiers. [4.4] CLASSIFICATION THROUGH ENSEMBLE OF CLASSIFIERS In this proposed approach, Simple and skilled forgery signature samples of writers are included in the testing phase and the writers used in training process are not included in testing process. The ensemble of 25 SVM Poly classifiers is used to classify the questioned signature samples as genuine or forge. Different number of Reference Signatures (RS) (3, 5, 7, 9, 11, 13, and 15) is used against the questioned signature sample for classification. Performance metrics FRR, FAR and AER are used to evaluate the efficiency of ensemble. False acceptance rate is computed for random, simple and skilled forgeries of the genuine signatures. [5] EXPERIMENTAL RESULTS In this work, simple and simulated forgery signature samples are not included in the training of classifiers that means classifiers are trained using only genuine and random forgery signature samples. This is because the simple and skilled forgery samples may not be available at the development of the system for most of the applications. MATLAB 2013a is used to carry out the experiments using 260 writers database. Experiments are performed by using ensemble of SVM - Poly classifiers. The performance of ensemble SVM Poly classifiers is computed using geometric, uniform rotation invariant LBP and hybrid (geometric plus uniform rotation invariant LBP) feature sets. The classification performance of individual classifier of ensemble during training phase using geometric, uniform rotation invariant LBP and hybrid (geometric plus uniform rotation invariant LBP) features is represented in [Figure-4], [Figure-5], and [Figure-6], respectively. The performance of the ensemble of classifiers in testing phase is presented in [Table-1], [Table-2] and [Table-3] for geometric, uniform rotation invariant LBP and hybrid (geometric plus uniform rotation invariant LBP) features, respectively, in terms of FRR, FAR and AER using max fusion rule. The experiment using hybrid feature set along with ensemble of SVM Poly classifiers reported 5.00 FRR and 0.00, 0.00, and 1.00 FAR for random, simple and skilled forgeries of genuine signature, respectively. This employs that FAR 0.33% is obtained while keeping false rejection rate 5.00% through proposed approach. Ashok Kumar and Karamjit Bhatia 7

8 REDUCING FALSE ACCEPTANCE RATE IN OFFLINE WRITER INDEPENDENT SIGNATURE VERIFICATION SYSTEM THROUGH ENSEMBLE OF CLASSIFIERS Figure: 4. Performance of individual classifier of ensemble using geometric features Figure: 5. Performance of individual classifier of ensemble using uniform rotation invariant LBP features Figure: 6. Performance of individual classifier of ensemble using hybrid features 8

9 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN Table 1: Performance of ensemble of classifiers using geometric features RS FRR FAR Random Simple Skilled AER Table 2: Performance of ensemble of classifiers using uniform rotation invariant LBP features RS FRR FAR Random Simple Skilled AER Table 3: Performance of ensemble of classifiers using hybrid features RS FRR FAR Random Simple Skilled AER The comparison of the performance between the proposed writer independent HSV system and the existing writer independent HSV systems is presented in [Table-4]. [6] CONCLUSION In this work, the performance evaluation of ensemble of SVM Poly classifiers based on geometric and uniform rotation invariant local binary pattern feature sets is carried out using 260 writers signatures database. In the proposed approach, the writers used in training are not used in testing the performance of offline HSV system. That means offline HSV system is able to classify the signature sample of unknown writers as genuine or forge without retraining the ensemble of classifiers. It is observed from the experiments, the performance of hybrid (geometric plus uniform rotation invariant LBP) features is better than geometric and Ashok Kumar and Karamjit Bhatia 9

10 REDUCING FALSE ACCEPTANCE RATE IN OFFLINE WRITER INDEPENDENT SIGNATURE VERIFICATION SYSTEM THROUGH ENSEMBLE OF CLASSIFIERS uniform rotation invariant local binary pattern features. Comparative study of the proposed method with existing methods for writer independent HSV system reveals that the proposed method outperforms the existing methods. In nutshell, it is concluded that a robust writer independent offline handwritten signature verification system with reduced false acceptance rate can be developed using ensemble of SVM - Poly classifiers along with geometric and uniform rotation invariant local binary pattern feature sets. Table 4: Comparative results of proposed and existing writer independent HSV systems S.N Authors Classifier FRR FAR Random Simple Skilled AER 1 C. Santos et al. [4] (2004) N N D. Bertolini et al. [2] (2010) SVM D. Rivard et al. [5] (2011) SVM R. Kumar et al.[7] (2012) NN & SVM-RBF G. Eskander et al. [6] (2012) SVM J. Swanepoel et al. [8] (2012) LDF &QDF G. Eskander et al. [9] (2013) SVM Hamadene A. et al.[10] (2016) OC-SVM Luiz Hafemann [11] (2016) SVM-RBF Proposed Approach (2017) REFERENCES Ensemble of SVM-Poly [1] Luiz S. Oliveira, Edson Justino, and Robert Sabourin, Offline signature verification using writer independent approach, IEEE International Joint Conference on Neural Networks, Orlando, Florida, USA, pp , [2] Bertolini D., Oliveir L.S., Justino E. and Sabourin R., Reducing forgeries in writer independent offline signature verification through ensemble of classifiers, Elsevier Journal of Pattern Recognition, Volume 43, Issue 1, pp , [3] E. Justino, F. Bortolozzi, and R. Sabourin, Off-line signature verification using hmm for random, simple and skilled forgeries, 6th International Conference on Document Analysis and Recognition, Seattle, WA, USA, pp , [4] Cesar Santos, Edson J. R. Justino, Flávio Bortolozzi, and Robert Sabourin, An offline signature verification method based on the questioned document expert s approach and a neural network classifier, IEEE 9th Int l Workshop on Frontiers in Handwriting Recognition, Washington, DC, USA, pp , [5] Dominique Rivard, Eric Granger and Robert Sabourin, Multi-feature extraction and selection in writer indepen-dent offline signature verification, International Journal on Document Analysis and Recognition, Volume 16, Issue 1, pp. 1 21,

11 International Journal of Computer Engineering and Applications, Volume XI, Issue VIII, August 17, ISSN [6] George S. Eskander, Robert Sabourin and Eric Granger, Adaptation of writer independent systems for offline signature verification, International Conference on Frontiers in Handwriting Recognition, Bari, Italy, pp , [7] Kumar R., Sharma J., and Chanda B., Writer independent offline signature verification using surroundedness feature, Pattern Recognition Letters, Volume 33, Issue 3, pp , [8] Jacques Swanepoel and Johannes Coetzer, Writer specific dissimilarity normalisation for improved writer inde- pendent signature verification, IEEE International Conference on Frontiers in Handwriting Recognition, Bari, Italy, pp , [9] George S. Eskander, Robert Sabourin, and Eric Granger, Hybrid writer independent writer dependent offline signature verification system, IET Biometrics, Volume 2, Issue. 4, pp , [10] Assia Hamadene and Youcef Chiban, One class writer independent offline signature verification using feature dissimilarity thresholding, IEEE Transactions on Information Forensics and Security, Volume 11, No. 6, pp , [11] Luiz G. Hafemann, Robert Sabourin, and Luiz S. Oliveira, Writer independent feature learning for offline signature verification using deep convolutional neural networks, International Joint Conference on Neural Networks, Vancouver, Canada, pp , [12] E. Pekalska, and R.P.W. Duin, Dissimilarity Representations Allow for Building Good Classifiers, Pattern Recognition, Volume 23, Issue 8, pp , [13] Hetal J. Vala and Prof. Astha Baxi, A Review on Otsu Image Segmentation Algorithm, International Journal of Advanced Research in Computer Engineering & Technology, Volume 2, Issue 2, pp , 2013 [14] Namratha M, Dr. S. Natarajan, Illustration of Usage of Local Binary Patterns for Feature Extraction in Face Recognition, International Journal of Emerging Technology and Advanced Engineering Volume 3, Issue 6, pp , [15] Timo Ahonen, Jiri Matas, Chu He, and Matti Pietikiainen1, Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features, 16th Scandinavian Conference, SCIA Oslo, Norway, pp , [16] H.R. Eghtesad Doost, and M. C. Amirani, Texture Classification with Local Binary Pattern Based on Continues Wavelet Transformation, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 10, pp , [17] P. Zhang, Model Selection via Multifold Cross Validation, Annals of Statistics, Volume 21, No. 1, pp , Author[s] brief Introduction Ashok Kumar has 15 years of teaching experience and has interest in the field of digital image processing. He is an Associate Professor in the Department of Computer Applications, Invertis University Bareilly, India. He is pursuing Ph.D. in Computer Science from Gurukula Kangri Vishwavidyalaya, Haridwar, India. Karamjit Bhatia received the M.Phil. (Computer Applications) in 1998 from University of Roorkee, Roorkee (now IIT Roorkee), India and Ph.D. (Computer Science) in 2001 from Gurukula Kangri Vishwavidyalaya, Haridwar, India. Currently, he is working asa Ashok Kumar and Karamjit Bhatia 11

12 REDUCING FALSE ACCEPTANCE RATE IN OFFLINE WRITER INDEPENDENT SIGNATURE VERIFICATION SYSTEM THROUGH ENSEMBLE OF CLASSIFIERS Professor at Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India. His research interests include Distributed Systems, Image Processing, and Mobile Adhoc Networks. He has published around 40 research papers in International Journals of repute and National/ International Conference proceedings. Fiveresearch scholars have been awarded Ph.D. degrees under his supervision. Corresponding Address: Ashok Kumar Invertis University, NH-24, Bareilly, Uttar Pradesh, India, Pin Code: Mobile Number: