关键词: 表情识别, 人脸分割, 卷积神经网络, 实时

Abstract:

Facial Expression Recognition（FER）in complex scenes is more challenging than that in a controlled environment due to the influencing factors such as facial posture, occlusion and illumination. Aiming at increasing the low recognition accuracy of FER in complex scenes, as well as the low recognition efficiency caused by complicated network structure in the process of FER, a real-time architecture for FER in complex scenes based on face region segmentation is proposed. It is composed of a face segmentation network called FsNet（Face segmentation Network） and an expression recognition network called TcNet （Tiny classification Network）. FsNet will segment the face area that is most relevant to FER to improve the recognition accuracy of TcNet, the training data applied in it is built from existing databases. The structures for both FsNet and TcNet are designed to be simple enough to ensure the real-time requirements for the architecture. Experiments on two natural expression databases（FER-2013 and RAD-DB） show that the face segmentation method is beneficial to improve the recognition effect of FER in complex scenes, and the overall architecture achieves good performance while guaranteeing real-time demand.

Key words: facial expression recognition, face segmentation, Convolutional Neural Network（CNN）, real time