Deep Learning-Based Classroom Fatigue Detection System

This paper integrates advanced computer vision techniques and machine learning algorithms, leveraging tools such as OpenCV and Dlib, to develop a deep learning-based classroom fatigue detection system. The system enables real-time monitoring of students' facial features, particularly dynamic changes in eye and head movements. Additionally, it incorporates 3D face model matching technology to enhance the precision of head posture and expression analysis. During the practical testing, the proposed system achieves an accuracy rate of about 93%, surpassing traditional PERCLOS-based detection methods. This research not only provides educators and students with a robust fatigue monitoring tool but also assists teachers in dynamically adjusting their pedagogical strategies for improved learning outcomes.