Classroom Video Image Emotion Analysis Method for Online Teaching Quality Evaluation

Abstract

Classroom emotion is an important dimension to evaluate teaching effect, and the application of image processing to online teaching emotion analysis has become an inevitable trend of development. Aiming at the problems of low accuracy of expression recognition, unclear emotion scheme for online teaching evaluation, and low applicability of expression recognition model in existing methods, this paper conducts a research on classroom video image emotion analysis method for online teaching quality evaluation. First, the classroom video image emotion analysis task is divided into facial expression recognition task and facial feature point location task, and multi-task learning is carried out to achieve real-time switching between the two tasks for different types of input. The tag attention mechanism is proposed to deeply mine the key areas of the face in the classroom video image, so as to maintain the compactness of the distribution of the center sample of the classroom video image and its neighborhood samples in the feature space. Finally, based on the expression activity of teachers and students in the online classroom, the online classroom teaching emotion is analyzed, and the online teaching quality is evaluated from the side. The experimental results verify the validity of the model.