Affiliations: [a] College of Engineering, Hebei Normal University, Shijiazhuang, China
| [b]
Vocational and Technical College of Hebei Normal University, Shijiazhuang, China
| [c] Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China
Correspondence:
[*]
Corresponding author. Shuang Zhang, College of Engineering, Hebei Normal University, Shijiazhuang, 050000, China. E-mail: zshuang@hebtu.edu.cn.
Abstract: Compared with other facial expression recognition, classroom facial expression recognition should pay more attention to the feature extraction of a specific region to reflect the attention of students. However, most features are extracted with complete facial images by deep neural networks. In this paper, we proposed a new expression recognition based on attention mechanism, where more attention would be paid in the channel information which have much relationship with the expression classification instead of depending on all channel information. A new classroom expression classification has also been concluded with considering the concentration. Moreover, activation function is modified to reduce the number of parameters and computations, at the same time, dropout regularization is added after the pool layer to prevent overfitting of the model. The experiments show that the accuracy of our method named Ixception has an maximize improvement of 5.25% than other algorithms. It can well meet the requirements of the analysis of classroom concentration.
