Attention-constraint facial expression recognition

To make full use of existing inherent correlation between facial regions and expression, we propose an attention-constraint facial expression recognition method, where the prior correlation between facial regions and expression is integrated into attention weights for extracting better representation. The proposed method mainly consists of four components: feature extractor, local self attention-constraint learner (LSACL), global and local attention-constraint learner (GLACL) and facial expression classifier. Specifically, feature extractor is mainly used to extract features from overall facial image and its corresponding cropped facial regions. Then, the extracted local features from facial regions are fed into local self attention-constraint learner, where some prior rank constraints summarized from facial domain knowledge are embedded into self attention weights. Similarly, the rank correlation constraints between respective facial region and a specified expression are further embedded into global-to-local attention weights when the global feature and local features from local self attention-constraint learner are fed into global and local attention-constraint learner. Finally, the feature from global and local attention-constraint learner and original global feature are fused and passed to facial expression classifier for conducting facial expression recognition. Experiments on two benchmark datasets validate the effectiveness of the proposed method.