Bayesian face recognition using a Markov chain Monte Carlo method

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 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.