Bayesian MCMC for Biometric Person Authentication Incorporating On-line Signature Trajectories

M. Kondo, D. Muramatsu, M. Sasaki, and T. Matsumoto (Japan)


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


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.

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