Classroom Expression Recognition Based on Deep Learning

In the field of education, classroom expression recognition technology has important application value. It can help teachers understand students' emotions and learning attitudes and adjust teaching strategies in time to improve learning outcomes. However, in a classroom environment with a large number of students, it is difficult for teachers to monitor individual learning progress, resulting in low classroom management efficiency, insufficient teaching personalization, and imperfect student evaluation mechanisms. To address the above problems, this paper constructs a dataset covering five typical classroom expressions (resistance, confusion, understanding, fatigue, and neutrality). To address the problem of insufficient data volume, this paper introduces a data enhancement method based on generative adversarial networks to improve the diversity and quality of the dataset. At the same time, a multi-attention fusion network (MAF-ER) is proposed, which combines spatial attention mechanism, channel attention mechanism, and self-attention mechanism. Experimental results show that the expression recognition accuracy of the MAF-ER algorithm reaches 88.34%, which is better than the baseline method. The ablation experiment further verifies the effectiveness of the multi-level attention (MLA) mechanism. The results show that the proposed method has a high accuracy in expression recognition tasks and provides strong support for smart classrooms.