In this paper we propose a new approach to video based face recognition. Our work is based on the Sparse Classification approach which assumes that each test sample can be formed by a linear combination of the training samples of the correct class. Based on this assumption, we formulate the classification problem as one of joint sparse recovery of Multiple Measurement Vectors (MMV). This requires solving an NP hard problem. This problem has not been solved earlier; thus we derive an algorithm for solving it. The experimental evaluation is carried on the VidTIMIT database. The proposed method is compared against an HMM based method for video based face recognition and the modified Sparse Classification method. The results show that the proposed method outperforms both these methods.
['Angshul Majumdar', 'Rabab K. Ward']
Face recognition from video: An MMV recovery approach