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Maximum Confidence Hidden Markov Modeling for Face Recognition

This paper presents a hybrid framework of feature extraction and hidden Markov modeling (HMM) for two-dimensional pattern recognition. Importantly, we explore a new discriminative training criterion to assure model compactness and discriminability. This criterion is derived from the hypothesis test theory via maximizing the confidence of accepting the hypothesis that observations are from target HMM states rather than competing HMM states. Accordingly, we develop the maximum confidence hidden Markov modeling (MC-HMM) for face recognition. Under this framework, we merge a transformation matrix to extract discriminative facial features. The closed-form solutions to continuous-density HMM parameters are formulated. Attractively, the hybrid MC-HMM parameters are estimated under the same criterion and converged through the expectation-maximization procedure. From the experiments on the FERET database and GTFD, we find that the proposed method obtains robust segmentation in the presence of different facial expressions, orientations, and so forth. In comparison with the maximum likelihood and minimum classification error HMMs, the proposed MC-HMM achieves higher recognition accuracies with lower feature dimensions.