Dense Attention Network for Facial Expression Recognition in the Wild

Abstract

Recognizing facial expression is significant for human-computer interaction system and other applications. A certain number of facial expression datasets have been published in recent decades and helped with the improvements for emotion classification algorithms. However, recognition of the realistic expressions in the wild is still challenging because of uncontrolled lighting, brightness, pose, occlusion, etc. In this paper, we propose an attention mechanism based module which can help the network focus on the emotion-related locations. Furthermore, we produce two network structures named DenseCANet and DenseSANet by using the attention modules based on the backbone of DenseNet. Then these two networks and original DenseNet are trained on wild dataset AffectNet and lab-controlled dataset CK+. Experimental results show that the DenseSANet has improved the performance on both datasets comparing with the state-of-the-art methods.