On Video Based Face Recognition Through Adaptive Sparse Dictionary

Sparse representation-based face recognition has gained considerable attention recently due to its robustness against illumination and occlusion. Recognizing faces from videos has become a topic of importance to alleviate the limit of information content in still images. However, the sparse recognition framework is not applicable to video-based face recognition due to its sensitivity towards pose and alignment changes. In this paper, we propose a video-based face recognition method which improves upon the sparse representation framework. Our key contribution is an intelligent and adaptive sparse dictionary that updates the current probe image into the training matrix based on continuously monitoring the probe video through a novel confidence criterion and a Bayesian inference scheme. Due to this novel approach, our method is robust to pose and alignment and hence can be used to recognize faces from unconstrained videos successfully. Moreover, in a moving scene, camera angle, illumination and other imaging conditions may change quickly leading to performance loss in accuracy. In such situations, it is impractical to re-enroll the individual and re-train the classifiers on a continuous basis. Our novel approach addresses these practical issues. Experimental results on the well known YouTube Face database demonstrates the effectiveness of our method.