文献类型：会议论文

英文题名：Research on an Improved Model for Expression Recognition in Classrooms

作者：Li, Hui[1];Li, Xing[2];Zhang, Yuxuan[3];Chen, Hongqian[3]

第一作者：李慧

通讯作者：Li, H[1]

机构：[1]Beijing Union Univ, Management Coll, Beijing 100101, Peoples R China;[2]Shandong Labor Vocat & Tech Coll, Dept Intelligent Mfg, Jian 250300, Shandong, Peoples R China;[3]Beijing Technol & Business Univ, Sch Comp & Artificial Intelligence, Beijing 100048, Peoples R China

第一机构：北京联合大学管理学院

通讯机构：[1]corresponding author), Beijing Union Univ, Management Coll, Beijing 100101, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

会议论文集：9th International Conference on Electronic Technology and Information Science (ICETIS)

会议日期：MAY 17-19, 2024

会议地点：Hangzhou, PEOPLES R CHINA

语种：英文

外文关键词：Facial expression recognition; Online classroom; Classroom video analysis; Depthwise separable convolution; Residual network; Global average pooling

摘要：Aiming at the real-time demand of expression recognition in online classroom application, this paper proposes an improved model to balance the real-time and accuracy of expression recognition. The optimization model can comprehensively consider the recognition time and recognition accuracy. As a result, it can better support the real-time understanding of students' class status in the process of online teaching. This paper mainly improves the model from the following aspects. Firstly, in the input stream structure, large convolution core and small pool core are used to reduce the amount of calculation of the training network. Secondly, in the intermediate stream structure, the depth separable convolution is used to make full use of the spatial correlation and channel correlation of the images in the video. Thirdly, in the intermediate flow structure, the deep separable convolution and residual network are fused into a hybrid module to reduce over fitting and enhance the generalization ability of the model. Finally, in the output stream structure, the global average pooling layer is used to replace the full connection layer to improve the efficiency of expression recognition. Through detailed experimental comparison and model analysis, the model improvement scheme proposed in this paper can occupy a good advantage in an ideal range, considering the training time, recognition time and recognition accuracy. The optimization model can be used to obtain students' expressions and analyze students' online class status through students' video data.