Abstract
A regional hidden Markov model (RHMM) for automatic facial expression recognition in video sequences is proposed. Facial action units are described by RHMMs for the states of facial regions: eyebrows, eyes and mouth registered in a video. The tracked facial feature points in the spatial domain form observation sequences that drive the classification process. It is shown that the proposed technique outperforms other methods reported in the literature for the person-independent case tested with the extended Cohn-Kanade database.
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