A Markov chain Monte Carlo algorithm for bayesian dynamic signature verification

D. Muramatsu, M. Kondo, M. Sasaki, S. Tachibana, T. Matsumoto

研究成果: Article査読

41 被引用数 (Scopus)


Authentication of handwritten signatures is becoming increasingly important. With a rapid increase in the number of people who access Tablet PCs and PDAs, online signature verification is one of the most promising techniques for signature verification. This paper proposes a new algorithm that performs a Monte Carlo based Bayesian scheme for online signature verification. The new algorithm consists of a learning phase and a testing phase. In the learning phase, semi-parametric models are trained using the Markov Chain Monte Carlo (MCMC) technique to draw posterior samples of the parameters involved. In the testing phase, these samples are used to evaluate the probability that a signature is genuine. The proposed algorithm achieved an EER of 1.2% against the MCYT signature corpus where random forgeries are used for learning and skilled forgeries are used for evaluation. An experimental result is also reported with skilled forgery data for learning.

ジャーナルIEEE Transactions on Information Forensics and Security
出版ステータスPublished - 2006 3月 1

ASJC Scopus subject areas

  • 安全性、リスク、信頼性、品質管理
  • コンピュータ ネットワークおよび通信


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