文献类型：期刊文献

英文题名：FERGCN: facial expression recognition based on graph convolution network

作者：Liao, Lei[1];Zhu, Yu[1,2];Zheng, Bingbing[1];Jiang, Xiaoben[1];Lin, Jiajun[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Engn Res Ctr Internet Things Resp Med, Shanghai 200032, Peoples R China

年份：2022

卷号：33

期号：3

外文期刊名：MACHINE VISION AND APPLICATIONS

收录：;EI(收录号：20221311845404);WOS:【SCI-EXPANDED(收录号:WOS:000772075900001)】；

基金：The authors greatly appreciate the financial supports of Natural Science Foundation of Shanghai under Grant 19ZR1413400, National Natural Science Foundation of China under Grant 82170110, Science and Technology Commission of Shanghai Municipality under Grant 20DZ2254400.

语种：英文

外文关键词：Expression recognition; Graph convolutional network; Deep learning; In-the-wild data

摘要：Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment. The proposed FERGCN includes three essential parts. First, a feature extraction module is designed to obtain the global feature vectors from convolutional neural networks branch with triplet attention and the local feature vectors from key point-guided attention branch. Then, the proposed graph convolutional network uses the correlation between global features and local features to enhance the expression information of the non-occluded part, based on the topology graph of key points. Furthermore, the graph-matching module uses the similarity between images to enhance the network's ability to distinguish different expressions. Results on public datasets show that our FERGCN can effectively recognize facial expressions in real environment, with RAF-DB of 88.23%, SFEW of 56.15% and AffectNet of 62.03%.