Emotion Recognition in Complex Classroom Scenes Based on Improved Convolutional Block Attention Module Algorithm

This study provides a deep learning-based intelligent recognition technology for student facial expressions in the classroom. This technology realizes the recognition of students' facial expressions and provides a practical approach for classroom assessment and teacher improvement of teaching methods toward achieving smart education. An improved hybrid attention mechanism is designed for the student classroom. The facial expression recognition model addresses issues of instability in the recognition process of student facial expressions in the classroom, the high redundancy of parameters in traditional convolutional neural networks, and the long training time and slow convergence prone to overfitting. In the image modality data, this study proposes a hybrid attention mechanism in the deep neural network model before feature fusion to extract network features with stronger representational capability, enhance the prediction performance of deep neural networks, and improve the interpretability of the model. The improved hybrid attention mechanism is introduced into the deep neural network by modifying the convolutional block attention module with shortcut connections, deepening the network depth of the attention module and enabling it to learn the weight information among feature channels and spatial regions effectively. The proposed student facial expression recognition model achieves an accuracy of 88.71% on the publicly available RAF-DB dataset and an accuracy of 86.14% on the self-collected real classroom teaching video dataset. The proposed technology can be applied in the education field to evaluate student engagement automatically, provide personalized teaching guidance and learning analytics for teachers, and promote the development of intelligent education.