A discriminative dynamic framework for facial expression recognition in video sequences

Facial expression involves a dynamic process, leading to the variation of different facial components over time. Thus, dynamic descriptors are essential for recognising facial expressions. In this paper, we extend the spatial pyramid histogram of gradients to spatio-temporal domain to give 3-dimensional facial features. To enhance the spatial information, we divide the whole face region into a group of smaller local regions to extract local 3D features, and a weighting strategy based on fisher separation criterion is proposed to enhance the discrimination ability of local features. A multi-class classifier based on support vector machine is applied for recognising facial expressions. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme show that the proposed framework perform better than using the descriptor of simple concatenation. Compared with state-of-the-art methods, the proposed framework demonstrates a superior performance.