High Throughput Robust Face Recognition using SVD

Abstract

In practice, there is no guarantee that the collected data would cover all different occlusions for all identities of interest. Here proposed an iterative method to address the face identification problem with block occlusions of two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. The first describes a tailored loss function. The second describes the error image as having a specific low-rank image comparison structure. In this paper shown that joint characterization is effective for describing errors with spatial continuity. Our approach is computationally efficient due to the utilization of the alternating direction method of multipliers. Using of the fast iterative algorithm leads to the robust representation method, which is normally used to handle non-contiguous errors. Extensive results on representative face databases document the effectiveness of our method over existing robust representation methods with respect to both identification rates and computational time.