95 articles 

inria-00105160, version 1

A Sequential Monte Carlo Algorithm for Adaptation to Intersession Variability in On-line Signature Verification

Yudai Kato () 1, Daigo Muramatsu () 1, Takashi Matsumoto () 1

Tenth International Workshop on Frontiers in Handwriting Recognition (2006)

Abstract: Personal authentication is becoming increasingly important, and on-line signature verification is one of the most promising approaches to the authentication problem. A factor known as intersession variability in signatures causes deterioration of authentication performance. This paper proposes a new algorithm for overcoming this problem. We propose an algorithm that integrates a model parameter updating scheme in order to suppress deterioration in the authentication system. A model is provided for each user to calculate the score using fused multiple distance measures with respect to previous work. The algorithm consists of an updating phase in addition to a training phase and a testing phase. In the training phase, a model is generated via Markov Chain Monte Carlo for each individual. In the testing phase, the generated model determines whether a test signature is genuine. Finally, in the updating phase, the parameters are updated with test data by using a Sequential Monte Carlo algorithm. Several experiments were performed on a public database. The proposed algorithm achieved an EER of 6.39%, using random forgery for training and skilled forgery for testing.

  • 1:  Department of Electrical Engineering and Bioscience
  • Waseda University
  • Domain : Computer Science/Document and Text Processing
    Computer Science/Computer Vision and Pattern Recognition
  • Keywords : Online Signature Verification – Biometrics – Intersession Variability – Sequential Monte Carlo – Markov Chain Monte Carlo
  • Comment : http://www.suvisoft.com
  • inria-00105160, version 1
  • oai:hal.inria.fr:inria-00105160
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  • Submitted on: Tuesday, 10 October 2006 14:46:04
  • Updated on: Tuesday, 10 October 2006 16:56:53