Robust Video-based Face Recognition by Sequential Sample Consensus

This paper presents a novel video-based face recognition algorithm using a sequential sampling and updating scheme, named sequential sample consensus (SSC). Different from the existing approaches, the training video sequences serve as the sample space, and the person's identity in the testing sequence is characterized by an identity probability mass function (PMF) that is sequentially updated. For each testing frame, samples are randomly drawn from the sample space with the numbers of samples for each identity determined by the identity PMF. The testing frame is evaluated against the drawn samples to calculate the weights, and the sample weights are utilized for updating the identity PMF. The proposed algorithm is robust against misclassification caused by pose variations, and sensitive to identity switching during recognition. The algorithm is evaluated using both public and self-made databases, and shows better performance than other video-based face recognition approaches.