Abstract
Several online signature verification systems that use cameras have been proposed. These systems obtain online signature data from video images by tracking the pen tip. Such systems are very useful because special devices such as pen-operated digital tablets are not necessary. One drawback, however, is that if the captured images are blurred, pen tip tracking may fail, which causes performance degradation. To solve this problem, here we propose a scheme to detect such images and re-estimate the pen tip position associated with the blurred images. Our pen tracking algorithm is implemented by using the sequential Monte Carlo method, and a sequential marginal likelihood is used for blurred image detection. Preliminary experiments were performed using private data consisting of 390 genuine signatures and 1560 forged signatures. The experimental results show that the proposed algorithm improved performance in terms of verification accuracy.
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References
Plamondon, R., Lorette, G.: Automatic signature verification and writer identification - the state of the art. Pattern Recognition 22(2), 107–131 (1989)
Ratha, N.K., Connell, J., Bolle, R.: Enhancing security and privacy of biometric-based authentication systems. IBM Systems Journal 40(3), 614–634 (2001)
Munich, M.E., Perona, P.: Visual identification by signature tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 25(2), 200–217 (2003)
Yasuda, K., Muramatsu, D., Matsumoto, T.: Visual-based online signature verification by pen tip tracking. In: Proc. CIMCA 2008, pp. 175–180 (2008)
Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
Matsumoto, T., Yosui, K.: Adaptation and change detection with a sequential Monte Carlo scheme. IEEE Trans. on Systems, Man, and Cybernetics – part B: Cybernetics 37(3), 592–606 (2007)
Matsui, A., Clippingdale, S., Matsumoto, T.: Bayesian sequential face detection with automatic re-initialization. In: Proc. International Conference on Pattern Recognition (2008)
Rabiner, L., Juang, B.-H.: Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs (1993)
Ross, A.A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. Springer Science+Business Media, LLC, Heidelberg (2006)
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© 2009 Springer-Verlag Berlin Heidelberg
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Muramatsu, D., Yasuda, K., Shirato, S., Matsumoto, T. (2009). Camera-Based Online Signature Verification with Sequential Marginal Likelihood Change Detector. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_28
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DOI: https://doi.org/10.1007/978-3-642-03767-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
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