Online Signature Verification Based on User-Generic Fusion Model with Markov Chain Monte Carlo, Taking into Account User Individuality
Kyosuke Koishi*, Shintaro Kinoshita*, Daigo Muramatsu**,
and Takashi Matsumoto***
*Department of Electrical Engineering and Bioscience, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555, Japan
**Department of Electrical and Mechanical Engineering, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino-shi, Tokyo 180-8633, Japan
***Graduate School of Advanced Science and Engineering, Waseda University. 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555, Japan
Personal authentication is becoming an increasingly important problem. Online signature verification is one promising form of biometric authentication. However, the verification accuracy of online signature verification is not high enough and still needs to be improved. To do so, we previously proposed a user-generic fusion model. Although the verification accuracy was reasonably good, the proposed method cannot adequately take into account users’ individuality. In this paper, to further improve the verification accuracy, we propose a method that can take such individuality into account. First, in a training phase, we divide a training dataset into several groups. Then, several fusion models are generated using a parameterized family of nonlinear functions by a Markov chain Monte Carlo method. In an enrollment phase, in order to take into account users’ individuality, we introduce model reliability. The model reliability for each user and each model is different, enabling us to take users’ individuality into account. In a verification phase, a marginal likelihood is calculated for each group. Then, a verification score is calculated by a weighted sum of the marginal likelihoods from the groups, using the model reliability. To evaluate the performance of the proposed algorithm, we conducted experiments using the SVC2004 database. The verification accuracy was improved over the previous algorithm.
and Takashi Matsumoto, “Online Signature Verification Based on User-Generic Fusion Model with Markov Chain Monte Carlo, Taking into Account User Individuality,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.4, pp. 447-456, 2009.
-  A. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans., Circuits Syst. Video Tech., Vol.14, No.1, pp. 4-20, 2004.
-  T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, “Impact of Artificial “Gummy” Fingers on Fingerprint Systems,” Proc. of SPIE, Vol.4677, pp. 275-289, 2002.
-  S. Kinoshita, D. Muramatsu, and T. Matsumoto “Online signature verification based on user-generic fusion model with Markov chain Monte Carlo method,” ISPACS 2006, pp. 391-394, Dec., 2006.
-  R. Plamondon and G. Lorette, “Automatic signature verification and writer identification: the state of the art,” Pattern Recognition, Vol.22, No.2, pp. 107-131, 1989.
-  F. Leclerc and R. Plamondon, “Automatic signature verification: the state of the art 1989-1993,” Int. Journal of Pattern Recognition and Artificial Intelligence, Vol.8, No.3, pp. 643-660, 1994.
-  V. S. Nalwa, “Automatic on-line signature verification,” Proc. IEEE, Vol.85, No.2, pp. 215-239, 1997.
-  R. Plamondon and S. N. Srihari, “On-line and off-line handwriting recognition: A Comprehensive Survey,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.22, No.1, pp. 63-84, 2000.
-  J. Brault and R. Plamondon, “Segmenting handwritten signatures at their perceptually important point,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.15, No.9, pp. 953-957, 1993.
-  R. Plamondon, “The design of an on-line signature verification system: from theory to practice,” Int. Journal of Pattern Recognition and Artificial Intelligence, Vol.8, No.3, pp. 795-811, 1994.
-  Y. Sato and K. Kogure, “Online signature verification based on shape, motion, and writing pressure,” Proc. 6th Int. Conf. on Pattern Recognition, Vol.2, pp. 823-826, 1982.
-  I. Yoshimura, M. Yoshimura, and S. Matsuda, “Choice of multiple representative signatures for on-line signature verification using a clustering procedure,” Proc. Conf. of Int. Federation of Classification Societies, pp. 247-250, 1996.
-  L. L. Lee, T. Berger, and E. Avizer, “Reliable on-line human signature verification systems,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.18, No.6, pp. 643-647, 1996.
-  T. H. Rhee, S. J. Cho, and J. H. Kim, “On-line signature verification using model-guided segmentation and discriminative feature selection for skilled forgeries,” Proc. Int. Conf. on Document Analysis and Recognition, Seattle, Washington, pp. 645-649, 2001.
-  A. K. Jain, F. D. Griess, and S. D. Connell, “On-line signature verification,” Pattern Recognition, Vol.35, No.12, pp. 2963-2972, 2002.
-  J. Fierrez-Aguilar, L. Nanni, J. Lopez-Penalba, J. Ortega-Garcia, and D. Maltoni, “An on-line signature verification system based on fusion of local and global information,” Springer Lecture Note in Computer Science, Proc. Audio- and Video-Based Biometric Person Authentication, Vol.3546, pp. 523-532, 2005.
-  B. L. Van, S. Garcia-Salicetti, and B. Dorizzi, “Fusion of HMM’s likelihood and viterbipath for on-line signature verification,” Springer Lecture Notes in Computer Science, Proc. ECCV 2004 Int. Workshop, BioAW2004, Vol.3087, pp. 318-331, 2004.
-  M. E. Munich and P. Perona, “Visual identification by signature tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.25, No.2, pp. 200-217, 2003.
-  D. Muramatsu, M. Kondo, M. Sasaki, S. Tachibana, and T. Matsumoto, “A Markov chain Monte Carlo algorithm for Bayesian dynamic signature verification,” IEEE Trans. Information Forensic and Security, Vol.1, Issue 1, pp. 22-34, 2006.
-  D.-Y. Yeung, H. Chang, Y. Xiong, S. George, R. Kashi, T. Matsumoto, and G. Rigoll, “SVC2004: First international signature verification competition,” Springer Lecture Notes in Computer Science, Vol.3702, pp. 16-22, 2004.
-  BioSecure Multimodal Evaluation Campaign (BMEC),
-  S. Garcia-Salicetti, C. Beumier, G. Chollet, B. Dorizzi, J. Leroux-Les Jardins, J. Lunter, Y. Ni, and D. Petrovska-Delacretaz,“BIOMET: a multimodal person authentication database including face, voice, fingerprint, hand and signature modalities,” Springer Lecture Notes in Computer Science, 4th Int. Conf. on Audio and Video-Based Biometric Person Authentication, pp. 845-853, 2003.
-  J. Ortega-Garcia, J. Fierrez-Aguilar, D. Simon, J. Gonzalez, M. Faundez-Zanuy, V. Espinosa, A. Satue, and I. Hemaez, “MCTY baseline corpus: a bimodal biometric database,” IEE Proc. Vision, Image and Signal Processing, Vol.150, No.6, pp. 395-401, 2003.
-  R. Bellman, “Dynamic Programming,” Princeton University Press, 1957.
-  R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” JOHN WILEY & SONS, INC., Sec. 10.4.3, pp. 526-528, 2001.
-  D. J. C. MacKay, “Bayesian methods for adaptive models,” Ph.D. thesis, California Inst. Tech, 1991.
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