Identity Management in Face Recognition Systems
Biometrics and Identity Management
Pruned Resampling: Probabilistic Model Selection Schemes for Sequential Face Recognition
IEICE - Transactions on Information and Systems
A sequential monte carlo method for bayesian face recognition
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Video Face Tracking and Recognition with Skin Region Extraction and Deformable Template Matching
International Journal of Multimedia Data Engineering & Management
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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[A Unified Approach to Video Face Detection, Tracking and Recognition][Partial automation of database acquisition in the FAVRET face tracking and recognition systemusing a bootstrap approach] 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 indentification errors by more than 90% on sequences showing individuals smiling although such data was not used in training.