Method of student identification in the context of College Classroom

In order to study the learning behaviors of college students, we need to identify students with the monitoring videos in the context of college classroom. We analyze the main challenges of face recognition under classroom conditions, and propose a face recognition method via combining multi-feature with alignment preprocessing. Obviously, the more identification photos per student may improve the recognition accuracy and the computational complexity simultaneously. Aiming at the practical use, we not only study on the accuracy but also the efficiency of the proposed method. We use the practical classroom videos and standard face databases to explore the best feature fusion strategies and prove the universality of the method.