A Bayesian MCMC On-line Signature Verification

Mitsuru Kondo, Daigo Muramatsu, Masahiro Sasaki, Takashi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingChapter

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 skilled1 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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJosef Kittler, Mark S. Nixon
PublisherSpringer Verlag
Pages540-548
Number of pages9
ISBN (Electronic)9783540403029
DOIs
Publication statusPublished - 2003
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kondo, M., Muramatsu, D., Sasaki, M., & Matsumoto, T. (2003). A Bayesian MCMC On-line Signature Verification. In J. Kittler, & M. S. Nixon (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 540-548). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2688). Springer Verlag. https://doi.org/10.1007/3-540-44887-x_64