A Bayesian MCMC On-line Signature Verification

Mitsuru Kondo, Daigo Muramatsu, Masahiro Sasaki, Takashi Matsumoto

研究成果: Chapter

抜粋

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.

元の言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
編集者Josef Kittler, Mark S. Nixon
出版者Springer Verlag
ページ540-548
ページ数9
ISBN(電子版)9783540403029
DOI
出版物ステータスPublished - 2003

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2688
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Kondo, M., Muramatsu, D., Sasaki, M., & Matsumoto, T. (2003). A Bayesian MCMC On-line Signature Verification. : J. Kittler, & M. S. Nixon (版), 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); 巻数 2688). Springer Verlag. https://doi.org/10.1007/3-540-44887-x_64