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A Sequential Monte Carlo Method for Bayesian Face Recognition

  • Atsushi Matsui
  • Simon Clippingdale
  • Takashi Matsumoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)

Abstract

This paper proposes a Sequential Monte Carlo (SMC) learning algorithm for Bayesian probability distributions that describe model parameters in a video face recognition system based on deformable template matching. The new algorithm achieves significantly improved robustness of recognition against facial expressions and speech movements by comparison with a baseline batch MCMC (Markov Chain Monte Carlo) algorithm, at no additional computational cost. Experimental results demonstrate the effectiveness and computational efficiency of the new algorithm.

Keywords

Feature Point Face Recognition Markov Chain Monte Carlo Method Face Model Gabor Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Atsushi Matsui
    • 1
    • 2
  • Simon Clippingdale
    • 1
  • Takashi Matsumoto
    • 2
  1. 1.Science & Technical Research LaboratoriesNHK (Japan Broadcasting Corporation)TokyoJapan
  2. 2.Dept. of Electrical Engineering & BioscienceWaseda UniversityTokyoJapan

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