Abstract
A machine learning approach to off-line signature verification is presented. The prior distributions are determined from genuine and forged signatures of several individuals. The task of signature verification is a problem of determining genuine-class membership of a questioned (test) signature. We take a 3-step, writer independent approach: 1) Determine the prior parameter distributions for means of both “genuine vs. genuine” and “forgery vs. known” classes using a distance metric. 2) Enroll n genuine and m forgery signatures for a particular writer and calculate both the posterior class probabilities for both classes. 3) When evaluating a questioned signature, determine the probabilities for each class and choose the class with bigger probability. By using this approach, performance over other approaches to the same problem is dramatically improved, especially when the number of available signatures for enrollment is small. On the NISDCC dataset, when enrolling 4 genuine signatures, the new method yielded a 12.1% average error rate, a significant improvement over a previously described Bayesian method.
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Sabourin, R., Plamondon, R.: Preprocessing of handwritten signatures from image gradient analysis. In: Proceedings of the 8th International Conference on Pattern Recognition, pp. 576–579 (1986)
Sabourin, R., Genest, G., Prteux, F.J.: Off-line signature verification by local granulometric size distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 976–988 (1997)
Srihari, S.N., Xu, A., Kalera, M.K.: Learning strategies and classification methods for off-line signature verification. In: Proceedings of the 7th International Workshop on Frontiers in handwriting recognition (IWHR), pp. 161–166 (2004)
Srihari, S.N., Kuzhinjedathu, K., Srinivasan, H., Huang, C., Pu, D.: Signature verification using a bayesian approach. In: Srihari, S.N., Franke, K. (eds.) IWCF 2008. LNCS, vol. 5158, pp. 192–203. Springer, Heidelberg (2008)
Horn, B.: Robot vision. MIT Press (1986)
Munich, M.E., Perona, P.: Visual identification by signature tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 200–217 (2003)
Fang, C.H.L., Tang, Y.Y., Tse, K.W., Kwok, P.C.K., Wong, Y.K.: Off-line signature verification by the tracking of feature and stroke positions. Pattern Recognition 36, 91–101 (2003)
Lee, S., Pan, J.C.: Off-line tracing and representation of signatures. IEEE Transactions on Systems, Man and Cybernetics 22, 755–771 (1992)
Lin, C.C., Chellappa, R.: Classification of partial 2-d shapes using fourier descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 686–690 (1987)
Ammar, M., Yoshido, Y., Fukumura, T.: A new effective approach for off-line verification of signatures by using pressure features. In: Proceedings of the 8th International Conference on Pattern Recognition, pp. 566–569 (1986)
Guo, J.K., Doermann, D., Rosenfeld, A.: Local correspondence for detecting random forgeries. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 319–323 (1997)
Kalera, M.K., Srihari, S., Xu, A.: Off-line signature verification and identification using distance statistics. International Journal of Pattern Recognition and Artificial Intelligence, 228–232 (2003)
Srikantan, G., Lam, S., Srihari, S.: Gradient-based contour encoding for character recognition. Pattern Recognition 7, 1147–1160 (1996)
Zhang, B., Srihari, S.N.: Analysis of handwriting individuality using handwritten words. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition. IEEE Computer Society Press, Los Alamitos (2003)
Srihari, S.N., Cha, S., Arora, H., Lee, S.: Individuality of handwriting. Journal of Forensic Sciences 47(4), 858–872 (2002)
Zhang, B., Srihari, S.N.: Binary vector dissimilarity measures for handwriting identification. In: Proceedings of SPIE, Document Recognition and Retrieval, pp. 155–166 (2003)
Blankers, V., van den Heuvel, C., Franke, K., Vuurpijl, L.: The icdar 2009 signature verification competition. In: Tenth International Conference on Document Analysis and Recognition (ICDAR 2009) (2009)
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Pu, D., Ball, G.R., Srihari, S.N. (2009). A Machine Learning Approach to Off-Line Signature Verification Using Bayesian Inference. In: Geradts, Z.J.M.H., Franke, K.Y., Veenman, C.J. (eds) Computational Forensics. IWCF 2009. Lecture Notes in Computer Science, vol 5718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03521-0_12
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DOI: https://doi.org/10.1007/978-3-642-03521-0_12
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