关键词: 表情识别, 深度学习, 稀疏自编码器, 多信息融合

Abstract: As an important part of human-computer interaction system, facial expression recognition has been widely used in security monitoring, human-computer interaction and other fields, and has always been a research hotspot of computer vision. The traditional convolution neural network method usually extracts single face image or face marker as input of feature extraction and fails to consider the facial expression information in the whole face domain. In this paper, a deep learning facial expression recognition model based on three-channel multi-information fusion is proposed. The feature information of the whole facial expression image is extracted by taking the relative displacement of the facial expression calm to the mark point coordinate in the peak period as the input. The model combines sparse autoencoder to improve the efficiency of edge feature extraction. The experimental results show that compared with similar algorithms in this field, the algorithm model of this paper improves the accuracy of expression recognition.

Key words: facial expression recognition, deep learning, sparse autoencoder, multi-information fusion