Application of Facial Analysis Based on Convolutional Neural Network and Iterative Decision Tree for Teaching Evaluation in Smart Classrrom

Abstract

The creation of intelligent classrooms has been expedited by the rapid advancement of the Internet and computer vision, and intelligent teaching has increased the interactivity and effectiveness of learning. Teachers’ teaching and students’ classroom learning state ultimately affect the teaching effect. Students’ facial expressions during class can reflect emotional changes and the current learning state. The computer camera in the smart classroom collects students’ face image data, uses texture-based information, edge-based information, geometric information, and global and local feature extraction to identify and analyze and process the students’ facial expressions. Research has shown that the combination of expression recognition and an intelligent teaching classroom can accurately identify and analyze students’ emotions and learning status, and can effectively evaluate the teaching effect of the intelligent classroom, which helps to improve teaching quality and learning efficiency. Therefore, applying facial expression recognition in the intelligent teaching classroom has far-reaching significance.