Dynamic facial expression recognition using autoregressive models

A dynamic facial expression recognition method based on the auto-regressive (AR) models using combined features of both shape and texture features is proposed in this paper. The AR model is effective to model complicated facial motions. In this work, six AR models are first learned for six basic expressions based on the fusion of shape and texture features of the difference between the neutral image and expressive face image. The difference tends to focus the facial parts that are changed from the neutral to expressive face and eliminate the influence of identity of the facial image. The shape features are facial feature point displacements between the normalized neutral and expressive face images while the texture features are local texture. Then the AR models are used to generate the predicted sequence which is compared with the actual sequence. The corresponding expression is inferred from the most similar predicted sequence to the actual one. Finally a line segment based method is proposed to compute the similarity between the predicted and actual expression sequences. The experiments have been conducted based on the extended Cohn-Kanade database. Encouraging results suggest a strong potential for dynamic facial expression.