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Sequential Sample Consensus: A Robust Algorithm for Video-Based Face Recognition

This paper presents a novel video-based face recognition algorithm by using a sequential sampling and updating scheme, named sequential sample consensus. The proposed algorithm aims at providing a sequential scheme that can be applied to streaming video data. Different from existing approaches, the training video sequences serve as the sample space, and the person's identity in the testing sequence is characterized using an identity probability mass function (PMF) that is sequentially updated. For each testing frame, samples are randomly drawn from the sample space, and the numbers of samples for each identity are determined by the identity PMF. The testing frame is evaluated against the drawn samples to calculate the weights, and the sample weights are used for updating the identity PMF. Benefiting from the sampling procedure, the change in both the numbers and the weights of the samples for each individual leads to quick reaction of the algorithm. 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 datasets, and shows better performance than other video-based face recognition approaches.