Bayesian MCMC for biometric person authentication incorporating on-line signature trajectories

Mitsuru Kondo, Daigo Muramatsu, Masahiro Sasaki, Takashi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 pen-inclination 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 1825 genuine signatures and 4117 skilled 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 publicationProceedings of the IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
EditorsM.H. Hamza
Pages269-273
Number of pages5
Publication statusPublished - 2003 Dec 1
EventProceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications - Rhodes, Greece
Duration: 2003 Jun 302003 Jul 2

Publication series

NameProceedings of the IASTED International Conference on Signal Processing, Pattern Reconition, and Applications

Conference

ConferenceProceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications
CountryGreece
CityRhodes
Period03/6/3003/7/2

Keywords

  • Bayes
  • Biometrics
  • Markov Chain Monte Carlo
  • Pattern recognition
  • Person authentication
  • Signature verification

ASJC Scopus subject areas

  • Signal Processing
  • Development
  • Computer Vision and Pattern Recognition

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  • Cite this

    Kondo, M., Muramatsu, D., Sasaki, M., & Matsumoto, T. (2003). Bayesian MCMC for biometric person authentication incorporating on-line signature trajectories. In M. H. Hamza (Ed.), Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition, and Applications (pp. 269-273). (Proceedings of the IASTED International Conference on Signal Processing, Pattern Reconition, and Applications).