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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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
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Dernière modification le : mardi 10 octobre 2006 - 16:56:53
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  • HAL Id : inria-00105160, version 1

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Yudai Kato, Daigo Muramatsu, Takashi Matsumoto. A Sequential Monte Carlo Algorithm for Adaptation to Intersession Variability in On-line Signature Verification. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00105160〉

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