A sequential monte carlo method for bayesian face recognition

Authors:
Atsushi Matsui;Simon Clippingdale;Takashi Matsumoto
Affiliations:
Science & Technical Research Laboratories, NHK (Japan Broadcasting Corporation), Tokyo, Japan;Science & Technical Research Laboratories, NHK (Japan Broadcasting Corporation), Tokyo, Japan;Dept. of Electrical Engineering & Bioscience, Waseda University, Tokyo, Japan
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

Citing 3
Cited 2

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Information Theory, Inference & Learning Algorithms
Bayesian Face Recognition using a Markov Chain Monte Carlo Method

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Pruned Resampling: Probabilistic Model Selection Schemes for Sequential Face Recognition

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Video Face Tracking and Recognition with Skin Region Extraction and Deformable Template Matching

International Journal of Multimedia Data Engineering & Management

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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.