Facial Expression Recognition Using Cascaded Random Forest Based on Local Features

Automatic facial expression recognition (FER) is an interesting and challenging topic which has potential applications in natural human-computer interaction. Researches in this field have made great progress. However, continuous efforts should be made to further improve the recognition accuracy for practical use. In this paper, an effective method is proposed for FER using a cascaded random forest based on local features. First, the hybrid features of appearance and geometric features are extracted within the salient facial regions sensitive to different facial expressions; second, a cascaded random forest based on the hybrid local features is developed to classify facial expressions in a coarse-to-fine way. Extensive experiments show that the proposed method provides better performance compared to the state of the art on different datasets.