Recognition of Student Emotions in Classroom Learning Based on Image Processing

Abstract

During the learning process, students should keep positive emotions like delightfulness, cheerfulness, joy, and enthusiasm, and try to realize happy learning. This would improve learning efficiency and learning effect. However, most of the existing models cannot recognize negative discrete emotions and dimensional emotions, such as indignation and sadness, of students in classroom learning. To solve the problem, this paper deeply explores the recognition of student emotions in classroom learning based on image processing. Specifically, the authors designed a recognition model of emotional state of classroom learning, modeled the discrete emotions of students in classroom learning, and discussed the relationship between learning effect, response efficacy and stimulus. Furthermore, the generation process of student emotions in classroom learning was analyzed, and the recognition model of emotional state of classroom learning was finalized based on ResNet18. In addition, the recognition model was optimized by introducing the local importance pooling and adding a recalibration module. The experimental results verify the effectiveness of the constructed model.