Video Assisted Face Recognition in Smart Classroom

Student recognition in smart classrooms is challenging due to low resolution, occlusions, and various face orientations. We propose a video-based approach for robust face recognition. It firstly collects a continuous sequence of face images through inter-frame analysis. Then, it aggregates face features, via statistical elimination of outliers. We introduce a new consistency loss to address varying face resolutions, enabling the learning of scale-robust features. Experimental results demonstrate that our proposed approach improves recognition performance under various conditions compared to previous methods. In our experiments, it achieved the highest recognition accuracy (83.57%) for 8×8 resolution cases.