Study on Multi-task Student Emotion Recognition Methods Based on Facial Action Units

With the rapid development of intelligent education,it has become a trend to use artificial intelligence to improve the quality and efficiency of education.The emotional state of students,who are at the center of education,has a crucial impact on educational effectiveness.In order to study students' emotions in depth,this paper collects students' learning videos in classroom scenarios,including two contexts of listening to lectures and group discussions,and builds a multi-task students' emotion database accordingly.The face serves as a direct outward manifestation of the internal emotional state,which shows a strong correlation between AU and emotions.Based on this,this paper proposes a multi-task learning-based student emotion recognition model Multi-SER.The model explores the association relationship between individual AUs and students' emotions by combining the two tasks of AU recognition and students' emotion recognition,thereby improving the performance of the model in students' emotion recognition.In the multi-task experiment,the Multi-SER model achieves an accuracy of 80.87% in emotion recognition,which improves the effect by 3.11% compared to the single emotion recognition task model SE-C3DNet+.The experimental results show that the performance of the model in categorizing various emotions is improved by mining the correlations between AUs and emotions through multi-task learning.