文献类型：期刊文献

英文题名：Facial Expression Recognition Method Combined with Attention Mechanism

作者：Chen, Ming[1];Cheng, Junqiang[2];Zhang, Zhifeng[1];Li, Yuhua[1];Zhang, Yi[3]

第一作者：Chen, Ming

通讯作者：Chen, M[1]

机构：[1]Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450000, Henan, Peoples R China;[2]Europe Asia Hitech & Digital Technol Co Ltd, Zhengzhou 450000, Henan, Peoples R China;[3]Xinxiang Univ, Business Sch, Xinxiang 458000, Henan, Peoples R China

第一机构：Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450000, Henan, Peoples R China

通讯机构：[1]corresponding author), Zhengzhou Univ Light Ind, Coll Software Engn, Zhengzhou 450000, Henan, Peoples R China.

年份：2021

卷号：2021

外文期刊名：MOBILE INFORMATION SYSTEMS

收录：;EI(收录号：20214111003362);Scopus(收录号：2-s2.0-85116654532);WOS:【SCI-EXPANDED(收录号:WOS:000703306600001)】；

基金：This research was supported by the National Science Foundation of China (nos. 61975187 and 61902021) and Henan Science and Technology Research Project (nos. 212102210104 and 162102210214).

语种：英文

外文关键词：Face recognition

摘要：Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.