Real-time facial emotion recognition using lightweight convolution neural network

Abstract

In recent years, facial expression recognition has played an important role in the field of human-computer interaction, and the application of deep learning technology has enabled it to develop more rapidly. In this paper, we create a lightweight network model for real-time emotion classification to recognize student facial expressions. The system includes: using Haar cascade for face detection, combined with the idea of Xception to propose a lightweight CNN network model, using pre-activation in the residual block to optimize the model and reduce the impact of overfitting. The experimental results on the FER2013 database show that our model has a better expression classification effect than other state-of-the-art methods. Besides, our model uses fewer parameters, which reduces the complexity of network training. When real-time emotional recognition of students, the system can help teachers adjust their teaching methods according to the emotional state of students.