Camera-Based Online Signature Verification with Sequential Marginal Likelihood Change Detector

  • Authors:

  • Daigo Muramatsu;Kumiko Yasuda;Satoshi Shirato;Takashi Matsumoto

  • Affiliations:

  • Department of Electrical and Mechanical Engineering, Seikei University, Tokyo, Japan 180-8633;Department of Electrical Engineering and Bioscience, Waseda University, Tokyo, Japan 169-8555;Department of Electrical Engineering and Bioscience, Waseda University, Tokyo, Japan 169-8555;Department of Electrical Engineering and Bioscience, Waseda University, Tokyo, Japan 169-8555

  • Venue:
  • CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
  • Year:
  • 2009

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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.