Abstract An embedded hidden markov model (e-HMM) based approach for facial expression recognition is proposed. It makes use of an optimized set of observation vectors obtained from the 2D-DCT coefficients of the facial region of interest. The e-HMM is trained with segmental K-means algorithm and used for the facial expression recognition. The experimental results show the remarkable improvement of the performance and robustness of the facial expression recognition system.
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2008, Vol. 21 
