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A Bayesian MCMC On-line Signature Verification

  • Mitsuru Kondo
  • Daigo Muramatsu
  • Masahiro Sasaki
  • Takashi Matsumoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

Authentication of individuals is rapidly becoming an important issue. The authors have previously proposed a pen-input online signature verification algorithm. The algorithm considers writer’s signature as a trajectory of pen-position, pen-pressure and peninclination which evolves over time, so that it is dynamic and biometric. In our previous work, genuine signatures were separated from forgery signatures in a linear manner. This paper proposes a new algorithm which performs nonlinear separation using Bayesian MCMC (Markov Chain Monte Carlo). A preliminary experiment is performed on a database consisting of 1852 genuine signatures and 3170 skilled 1 forgery signatures from fourteen individuals. FRR 0.81% and FAR 0.87% are achieved. Since no fine tuning was done, this preliminary result looks very promising.

Keywords

Markov Chain Monte Carlo Hide Unit False Acceptance Rate False Rejection Rate Bayesian Markov Chain Monte Carlo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    T. Ohishi, Y. Komiya and T. Matsumoto. On-line Signature Verification using Pen Position, Pen Pressure and Pen Inclination Trajectories. Proc. ICPR 2000, Vol. 4, pp547–550, September, 2000.Google Scholar
  2. [2]
    H. Morita, D. Sakamoto, T. Ohishi, Y. Komiya, T. Matsumoto. On-Line Signature Verifier Incorporating Pen Position, Pen Pressure and Pen Inclination Trajectories. Proc. 3rd AVBPA, Sweden, pp.318–323, June, 2001.Google Scholar
  3. [3]
    M. Kondo, D. Sakamoto, M. Sasaki and T. Matsumoto. A New Online Signature Verification Algorithm Incorporating Pen Velocity Trajectories. Proc. IEEE ISPACS 2002, Taiwan R.O.C. November, 2002.Google Scholar
  4. [4]
    D.J.C. MacKay, Bayesian Methods for Adaptive Models, PhD thesis, California Inst. Tech. 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Mitsuru Kondo
    • 1
  • Daigo Muramatsu
    • 1
  • Masahiro Sasaki
    • 1
  • Takashi Matsumoto
    • 1
  1. 1.Department of Electrical, Electronics and Computer Engineering Graduate School of Science and EngineeringWaseda UniversityTokyoJapan

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