Maximum confidence hidden Markov modeling for face recognition

Face recognition has been known as one of key applications to build high-performance surveillance or information security systems [1][2][3][4]. The statistical paradigm based on hidden Markov model (HMM) has been successfully developed for face recognition [3]. Generally, HMM is effective to represent time-series signal such as speech data where time alignment is a critical concern. It can be extended to align two-dimensional (2D) image signal and model spatial variations, e.g. facial expressions, orientations and beard, etc. Figure 1 displays the embedded HMM representation for face images with different facial features. In this work, we concern two issues in establishing HMMs for face recognition. The first issue involves the hybrid process of feature extraction and model estimation. Figure 1: Embedded HMM representation for facial images. Secondly, we develop a new discriminative training criterion derived by statistical hypothesis test theory. By maximizing the confidence towards accepting the hypothesis that sub-images are from target HMM state against the hypothesis that sub-images are from competing HMM states, the maximum confidence (MC) criterion is exploited for estimation of MC-HMMs, where the feature transformation W is naturally embedded. The facial features and HMM parameters are jointly estimated for discriminative face recognition. In recognition phase, we present a doubly Viterbi segmentation to obtain optimal state 1 of 4 Researc