Affective Video Classification Based on Spatio-temporal Feature Fusion

In this paper, we propose a novel affective video classification method based on facial expression recognition by learning the spatio-temporal feature fusion of actors´ and viewers´ facial expressions. For spatial features, we integrate Haar-like features into compositional ones according to the features´ correlation, and train a mid classifier during the period. Then this process is embedded into improved AdaBoost learning algorithm to obtain spatial features. And for temporal feature fusion, we adopt hidden dynamic conditional random fields (HDCRFs) based on HCRFs by introducing time dimension variable. Finally spatial features are embedded into HDCRFs to recognize facial expressions. Experiments on the well-known Cohn-Kanada database show that the proposed method has a promising recognition performance. And affective classification experimental results on our own videos show that most subjects are satisfied with the classification results.