Signature Identification using Dynamic and HMM Features and KNN Classifier

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1 2013 International Conference on Communication Systems and Network echnologies Signature Identification using Dynamic and HMM Features and KNN Classifier Ava ahmasebi 1, Hossein Pourghassem 2 Department of Electronic Engineering, Islamic Azad University, Najafabad branch Isfahan, Iran 1 ava_tahmasebi@sel.iaun.ac.ir 2 h_pourghasem@iaun.ac.ir Abstract: Dynamic feature-based signature recognition systems obtain more identification accuracy rate than static featurebased signature recognition systems. On the other hand, because of point to point comparing of captured signals, local features produce less error than general features. In this paper, a novel signature identification algorithm based on dynamic and Hidden Markov Models (HMM features and K- Nearest Neighbor (KNN classifier is proposed. In this algorithm, special point dynamic features of handwritten signatures are selected and normalized and then used as signature features. In the used HMM, different number of states in transition matrix for each signature are calculated and used as feature vector. his strategy not only reduces complexity computational but also is extracted features based on general characteristics of signature. he proposed algorithm is evaluated on the standard SVC2004 database. he obtained results are reported based on various values of parameters of the proposed algorithm and compared with presented approaches in the literature. Index erms signature identification, dynamic features, k- nearest neighbor, Hidden Markov Model features. I. INRODUCION oday, one of the conventional methods for human identificatios Signature verification. Although this strategy on biometric has middle accuracy than methods such as fingerprints, palm, gait, hand geometry and retina, but because of acceptability, particularly its cheapness and simplicity of banking business, it has many usages. Signature recognition techniques are classified based on the nature of their Database in static and dynamic groups. hese methods have beentroduced by Plamondon and Srihari [1]. In static methods Properties of the signature image data are extracted by scanning from documents [2], but special instruments, such as digital pen and tablet, are used to record the dynamic information of signatures (Fig 1. hus information such as position, velocity, pressure, Pen azimuth and Pen altitude are obtained. Features extracted from the dynamic methods can be local or general. General features applying mathematical solutions to the whole sign such as average velocity, standard deviation, and maximum pressure [2], [3], [4]. Fig 1: Dynamic signature capturing device General features are also obtained from special mathematical transformations such as discrete wavelet conversion [5] and Hough ransform [6],[7]. [9] Presents a set of geometric signature features for offline automatic signature verification based on the description of the signature envelope and the interior stroke distribution polar and Cartesian coordinates. he features have been calculated using 16 bits fixed-point arithmetic and tested with different classifiers, such as hidden Markov models, support vector machines, and Euclidean distance classifier. In [10] a method for off-line signature verification based on geometric feature extraction and neural network classificatios proposed. he role of signature shape description and shape similarity measure is discussed in the context of signature recognition and verification. Geometric features of input signature image are simultaneously examined under several scales by a neural network classifier. In this paper, we attempt to apply HMM model and KNN classifier to dynamic signature feature vectors for personal authentication. Firstly special points of signature are extracted. Dynamic features of these points such as position, normal pressure, path tangent angle, velocity, total Acceleration, Log radius of curvature, Pen azimuth and Pen altitude are used,then the feature vector for each class are applied to HMM. Given number of states, matrix of states obtained and reshaped to row vector, while reducing size of feature vector. KNN classifier is employed and the database entries are classified in the real and forged groups. he remaining sections are organized as follows. he proposed algorithm including signature verification system is introduced in Section 2. Section 3 presents KNN /13 $ IEEE DOI /CSN

2 classifier. Experimental results are shown section 4. Finally in section 5 we provide some conclusions. II. PROPOSED SIGNAURE VERIFICAION ALGORIHM Signature verification Algorithm includes these steps: signature acquisition, pre-processing, feature extraction, applying HMM too each feature vector, classification, comparison and decision process. Fig 2 shows the block diagram of proposed signature verification system. Signature of a person over time and multiple iterations is not identical. So preprocessing would provide similar conditions for accurate comparison of two signature patterns. Preprocessing includes smoothing processes and makes signals same size. Smoothing on both axes (X, Y is done separately by Gaussian filter [11]. Optional features should be separately classified and classes have been defined and possible decision boundaries clearly drawn. Biometric systems in general and the signature verification considered in this paper should be able to classify true and forged signatures. A. SPECIAL POINS SELECION In this paper, special points of signature which are the turning points of strokes are selected. Because they are the key points of signature and whose surroundings contain many dynamic and static characteristics, and have very strong discrimination. In Fig 3, P i is the point (X i,y i after the preprocessing, and A is the angel between P i i and two neighbors P i-m and P i+m. where m=1 and these three points are consecutive. he angle is obtained as follows: 1 ( PP ( PP i i m i i m A ( PP cos i i i m PP PP i i m i i m he point, which the first derivative of whose angel is smaller than zero and 0 A A i 145 are namely the special points [12]. Fig 4 shows sample signature with the marked special points. B. FEAURE EXRACION SVC2004 database [13] is one of the widely used signature database. Each signature is stored in a separate text file. In each signature file the first line stores a single integer (1 Fig 2: bock diagram of signature verification system Fig 3: Angle of special points Fig 4: Signature with the marked special points which is the total number of points in the signature. Each of the following lines corresponds to one point characterized by features listed in the following order: X-coordinate - scaled cursor position along the x-axis Y-coordinate - scaled cursor position along the y-axis ime stamp - system time at which the event was posted Button status - current button status (0 for pen-up and 1 for pen-down Azimuth - clockwise rotation of cursor about the z-axis Altitude - angle upward toward the positive z-axis Pressure - adjusted state of the normal pressure After selecting the special points, features of these points are extracted from signature files. able (1 shows features of special points which are used to verification. After normalizing features of each point of signatures, we locate them in row vectors. 202

3 ABLE 1: HE USED LOCAL FEAURES IN SIGNAURE IDENIFICAION Feature name Formula Horizontal position x t Vertical position y t Normal pressure p t Path tangent angle arctan( x / y otal velocity 2 2 v x t y t x velocity y velocity v v x y x t y otal acceleration 2 a v 2 ( v t ( Log radius of curvature log( / Pen azimuth Pen altitude z t l t v t t C. HIDDEN MARKOV MODEL: In this paper, the feature vectors obtained from previous section are applied to hidden Markov model. o fully define the topology of the HMM, one must first select the number of states, the allowed state transitions, and the probability density/mass function(s to use in modeling the hidden states emissions. raditionally the number of states is determined empirically, as the structure of the phenomenon to be modeled is usually unknown. For some state S i, the states that are allowed to directly follow it (sequentially in time denote the allowed state transitions. One may force a specific set of allowed transitions or let it be data driven. A standard topology is the left-to right model [14]. In the so-called left-to-right topology, shown Fig 5, all states except the last one may transition only to itself and its right neighbor while the last state may only transition to itself. Hence the order in the signatures database drawn from left to right; the left - right Markov model is used. III. CLASSIFICAION he k-nearest neighbors algorithm (KNN is a method for classifying objects based on closest training examples in the feature space. KNN is a type of instance-based learning, or lazy learning where the functios only approximated locally and all computatios deferred until classification. he k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors.he KNN technique can also be used for estimation of a-posteriori probabilities p ( x from a set of n labeled samples as follows: k i p ( x (2 k Where k defines the samples captured in a cell of volume v placed around x and k i of which turn out to be labeled ω i [15]. hat is, the estimate of the p ( x a posteriori probability that ω i is the state of nature is merely the fraction of the samples within the cell that are labeled ω i. Consequently, for minimum error rate we select the category most frequently represented within the cell. If there are enough samples and if the cell is sufficiently small, it can be shown that this will yield performance approaching the best possible. After obtaining the m m state matrices, each matrix converted to vector with size m 2 1 and we provide training sample by collecting all the vectors of various signatures in a matrix, then KNN classificatios applied. By changing the parameter k nearest neighborhood system performance is tested. IV. EXPERIMEN RESULS his section consists of a description of the database used for verifying the developed system performance and the description of the extensive experimental setup. A. Experimental data Fig 5: Left - Right Hidden Markov Model Another advantage of this model is feature reduction. So signals with great length can be represented as smaller number of HMM parameter thus decrease complexity computational. he used database [13] contains 40 sets of the signatures collected from different people, and each set consists of 20 genuine signatures and 20 skilled forgery signatures. o have patterns of signature, a WACOM (Graphier 4 with sampling rate of 100 HZ is used. he database contains 40*40 signatures. he 1600 created spatial-temporal images is used as the input of the HMM to get the state matrices. hen we connect all the rows of each individual s to obtain features vector. During the training process we select 3 genuine signatures as the reference signatures. he rest genuine signature and skilled forgery signatures are used as test samples. 203

4 B. Verification tests After the feature vectors extracted from applying HMM, it is required to decide a number of patterns to be chosen as reference comparison to compare and classify them. Equation (3 shows the linear scaling used for normalization of the vector F. F-mean(Freference Fnormalized (3 std(f reference Mean and standard deviation (std values used for explained feature vector (F reference are calculated for each signature separately. For proposed methods omplementing and testing, database was divided into two parts so that the number of 20 signatures as reference patterns, 20 inventors of patterns for training and others have been used for testing. For forger group also 20 signatures were randomly selected for each individual signature. o classify patterns by KNN method the Euclidean distance method is used to evaluate the distance of samples. Usually, the performance of a biometric system is expressed by some parameters. A decision made by a biometric system is either a genuine individual type of decision or an impostor type of decision. For each type of decision, there are two possible outcomes, namely, true or false. herefore, there are a total of four possible outcomes: A genuine individual is accepted, a genuine individual is rejected, ampostor is rejected, and ampostor is accepted. Outcomes 1 and 3 are correct, whereas outcomes 2 and 4 are incorrect. he confidence associated with different decisions may be characterized by the genuine distribution and the impostor distribution, which are used to establish the following two error rates [16]. False acceptance rate (FAR, which is defined as the probability of ampostor being accepted as a genuine individual. It is measured as the fraction of impostor score (matching score which involves comparing two biometric samples originating from different users exceeding the predefined threshold. False rejection rate (FRR, which is defined as the probability of a genuine individual being rejected as an impostor. It is measured as the fraction of genuine score (matching score which involves two samples of the same biometric trait of a user below the predefined threshold. FAR and FRR are dual of each other. A small FRR usually leads to a larger FAR, while a smaller FAR usually implies a larger FRR. able (2 shows result of classification derived with k = 9 and different S and the comparisons are in terms of FAR and FRR. ABLE 2: KNN CLASSIFICAION WIH DIFFEREN VALUES OF K IN KNN AND RANSIION MARIX Parameter S Parameter k FAR (% FRR (% It can obviously seen that the maximum classification accuracy obtained from S = 8. In other experiment we examined parameter of KNN. able (3 shows the classification result with S = 8 and different k that indicate acceptable results. ABLE 3: EVALUAE CLASSIFICAION PERFORMANCE BY CHANGING NUMBER OF NEIGHBORS Parameter k Parameter S FAR (% FRR (% So the best results obtained by placing simulated values S = 8 and k = 11 for the KNN classification based on HMM model. Fig 6 shows examples of signatures from the visually captured data sets for which the algorithm has a verification error rate greater than 11 percent. (a (d (g (b (e (h Fig 6: experimental samples: (a to (c are visually-acquired signatures, (d to (f are falsely rejected signatures, (g to (i are falsely accepted skilled forgery signatures (c (f (i 204

5 We compare our results with some other dynamic signature verification systems in terms of FAR and FRR which are shown able (4. ABLE 4: COMPARISON OF PROPOSED APPROACH WIH OHER PUBLISHED MEHODS Algorithm Method FAR (% FRR (% Houng Neural network et al [10] Ferrer HMM & SVM et al [9] Varagas et al [17] pseudo-cepstral coefficients & SVM Sansone et al [18] Proposed method SVM & HMM HMM & KNN From the results, it is clear that the proposed method achieves acceptable system performance when compared with similar systems. he main reason for this is that, in this approach, applying HMM to special points of signature gives us robust feature vector for classification. hus, these features have more influence on the final results. V. CONCLUSION Recent years have seen a significant increase in research activity directed at understanding all aspects of biometric information system representation and utilization for decision-making support, for use by public and security services, and for understanding the complex processes behind biometric matching and recognition. Although signature verification systems, based on general features were simpler and have less response time, but also have less accuracy. In this paper, using dynamic features of turning points of strokes, which are the key points of signature, encompass acceptable accuracy. In addition to reduce the size of calculates, efficiency of the system also improved taking Hidden Markov model, eventually KNN have good performance in our experiment and results illustrate the effectiveness of the method. [4] LL Lee,. Berger, E. Aviczer, "Reliable on-line human signature verification systems," IEEE rans. on Pattern Analysis and Machine, vol. 18, no. 6, pp , [5] H. Lei, V. Govindaraju, "A comparative study on the consistency of features in on-line signature verification," Pattern Recognition Letters, vol. 26, no. 15, pp , [6] P. Porwik, "he compact three stages method of the signature recognition," in Proc. of the 6th IEEE Int. Conference Computer Systems and Industrial Mnagment Applications, 2007, pp [7] VS. Nalwa, "Automatic On-line Signature Verification," Proceedings of the IEEE, vol. 85, no. 2, pp , [8] Kholmatov, B. YanikogluA. Kholmatov, B. Yanikoglu, "Identity authentication using improved online signature verification method," Pattern RecognitionLetters, Vol.26, No. 15, pp , [9] K. Huang, H. Yan, "Off-line signature verification based on geometric feature extraction and neural network classification," Pattern Recognition, Elsevier Science, vol. 30, no. 1, pp. 9-17, [10] M. Ferrer, J. Alonso, and C. ravieso, "Offline geometric parameters for automatic signature verification using fixedpoint arithmetic," IEEE ransactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp , [11] X. Ling, Y.Wang, Z. Zhang, Y. Wang, "On-line signature verification based on Gabor features," in wireless and optical communications conference (WOCC, Shanghai, 2010, pp [12] Hai, LUAN Fang-jun LIN Lan CHENG, "he Algorithm of On-line Handwritten Signature Verification," in 2nd international congress on signal and image processing CISP' 09, ianjin, 2009, pp [13] SVC; he First International Signature Verification Competition, 2nd National Electrical Enineering Conference - February [14] L. R. Rabiner, "A tutorial on hidden markov models and selected applications in speech recognition,", 1990, p [15] RO Duda, PE Hart and DG Stork, Pattern Recognition, 2nd ed.: John Wiley, [16] Md. Maruf Monwar, Marina L. Gavrilova, "Multimodal Biometric System Using Rank-Level Fusion Approach," IEEE RANSACIONS ON SYSEMS, MAN, AND CYBERNEICS PAR B: CYBERNEICS, vol. 39, no. 4, pp , AUGUS [17] C. Sansone and M. Vento, "Signature verification: Increasing performance by a multi-stage system," Pattern analysis & Applications, no. 3, p , [18] J. Vargas, M. Ferrer, C. ravieso, and J. Alonso, "Off-line signature verification based on high pressure polar distribution," in ICFHR08., Montereal, August 2008.x REFERENCES [1] R. Pelamondon, SN. Srihari, "On-line and off-line handwriting recognition: a comprehensive survey," IEEE rans. on Pattern Analysis, vol. 22, pp , [2] DZ. Lejtman, SE.George, "On-line handwritten signature verification using wavelets and back-propagation neural networks," in Proc. on the 6th Int. Conference on Document Analysis and Recognition, 2001, pp [3] Fierrez-Aguilar, L. Nanni, J. Lopez-Penalba, J. Ortega-Garcia and D. Maltoni., "An online signature verification system based on fusion of local and global information," in Conf. on Audio-and Video-Based Biometric Person Authentication, 2005, pp