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

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.
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Contributor : Anne Jaigu Connect in order to contact the contributor
Submitted on : Tuesday, October 10, 2006 - 2:46:04 PM
Last modification on : Friday, March 15, 2019 - 5:24:11 PM
Long-term archiving on: : Thursday, September 20, 2012 - 11:41:03 AM


  • HAL Id : inria-00105160, version 1



Yudai Kato, Daigo Muramatsu, Takashi Matsumoto. A Sequential Monte Carlo Algorithm for Adaptation to Intersession Variability in On-line Signature Verification. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). ⟨inria-00105160⟩



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