Method of student identification through college classroom surveillance videos using deep learning features and label propagation

For education or management, it is often necessary to identify students with their identification (ID) photos through the surveillance videos of the college classrooms. This is a typical application of ID photo based single-sample per-person (SSPP-ID) face recognition. After analyzing the main challenges, we propose a framework by combining deep learning method and label propagation algorithm together. It is composed of three sequential steps: the first step aims to partition the face image into several patches and get an unbalanced-patch based feature using ConvNets; In the second step, we select a few key-frames by using the log-likelihood ratio calculated by the Joint Bayesian model; The last step uses label propagation algorithm to propagate the labels from the key frames to the whole video by simultaneously incorporating constraints in temporal and feature spaces. The performance of the proposed method is evaluated on Movie Trailer Face Dataset and practical college class surveillance videos. Experiments with these challenging datasets validate the utility of the proposed method.