A new algorithm is proposed for face recognition by a Bayesian framework. Posterior distributions are computed by Markov chain Monte Carlo (MCMC). Face features used in the paper are those used in our previous work[1][2] based on the Elastic Graph Matching method. While our previous method attempts to optimize facial feature point positions so as to maximize a similarity function between each model and face region in the input sequence, the proposed approach evaluates posterior distributions of models conditioned on the input sequence. Experimental results show a rather dramatic improvement in robustness. The proposed algorithm eliminates almost all identification errors on sequences showing individuals talking, and reduces identification errors by more than 90% on sequences showing individuals smiling although such data was not used in training.
['Atsushi Matsui', 'Simon Clippingdale', 'Fumiki Uzawa', 'Takashi Matsumoto']
Bayesian face recognition using a Markov chain Monte Carlo method