Facial expression recognition using HMM with observation dependent transition matrix

An expression recognition technique is proposed based on the hidden Markov models (HMM) ability to deal with time sequential data and to provide time scale invariability as well as a learning capability. A feature vector sequence is used for this purpose, which relies on optical flow extraction, as well as directional filtering of the motion field. Segmentation and identification of important facial parts are preceding feature extraction. The HMM is enhanced with an observation dependent transition matrix, being able to cope with the dynamics of emotions and the severe complexity of expressions timing. Experimental results are included illustrating the effectiveness of this method.