Improved Generative Adversarial Networks for Student Classroom Facial Expression Recognition

Abstract

To assess students’ learning efficiency under different teaching modes, we used students’ facial expressions in the classroom as a study point. An enhanced generative adversarial network is presented. We designed a generator as an automatic coding-decoding combination in a cascade structure with a discriminator configuration. It can retain different expression intensity features to the maximum extent. We also added a new auxiliary classifier, which can classify different intensity features and improve the model’s recognition of detailed features of similar expressions, thus improving the comprehensive facial expression recognition accuracy. Our approach has a great advantage over the other facial expression recognition approaches on public datasets. Finally, we conduct experimental validation on the self-made student facial expression dataset in all cases. The experimental findings showed that our approach’s recognition accuracy is superior to that of other methods, demonstrating the method’s efficacy.