Robust and Low-Rank Representation for Fast Face Identification with Occlusions

On Maximizing Diffusion Speed Over Social Networks With Strategic Users

propose an iterative method to address the face identification problem with block occlusions.Our approach utilizes a robust representation based on two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. The first fits to the errors a distributionDescribed by a tailored loss function. The second describes the error image as having a specific structure (resulting in low-rank). We will show that this 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 (ADMM). A special case ofour fast iterative algorithm leads to the robust representation method which is normally used to handle non-contiguous errors (e.g., pixel corruption). Extensive results on representative facedatabases document the effectiveness of our method over existing robust representation methods with respect to both identification rates and computational time.Code is available at Github, where you can find implementa-tions of the F-LR-IRNNLS and F-IRNNLS ( fast version of theRRC) : ttps://github.com/miliadis/FIRC