Face tracking and recognition via incremental local sparse representation

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. We first develop a robust face tracking algorithm based on the local sparse appearance. This sparse representation model exploits both partial and spatial information of the face based on a covariance pooling method. Following in the face recognition stage, with the employment of a novel template update strategy, our recognition algorithm adapts the template to appearance change and reduces the influence of occlusion and illumination variation. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database. Our proposed method produces a high face recognition results on over 93% of all videos. The tracking results on challenging videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. On the challenging data set in which faces are undergo occlusion and illumination variation, our proposed method also consistently demonstrates a high recognition rate.