Robust Video Face Recognition Under Pose Variation

摘要

With the abundance of video data, the interest in more effective methods for recognizing faces from surveillance videos has grown. However, most algorithms proposed in this field have an assumption that each image set lies in a single linear subspace, or a mixture of linear subspaces. As a result, 3-dimensional shape information, which leads to the nonlinear transformation of face images, is ignored. This paper proposes a robust video face recognition across pose variation in video (RVPose) based on sparse representation. The key idea is performing alignment and recognition based on sparse representation simultaneously. Moreover, by considering that multi-pose faces of the same subject possess the same texture and 3-dimensional shape, RVPose aligns a sequence of faces with pose variations simultaneously, which is reduced to a 3-dimensional shape-constrained video alignment problem. Finally, aligned video sequence is recognized based on sparse represent. Experiments conducted on public video datasets demonstrate the effectiveness of the proposed algorithm.