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

TL;DR

This paper introduces a fast, robust face identification method that effectively handles block occlusions by modeling errors with a joint low-rank and distribution-based approach, improving accuracy and efficiency.

Contribution

It proposes a novel iterative algorithm combining low-rank and distribution-based error modeling for occlusion-robust face recognition, with computational efficiency via ADMM.

Findings

Outperforms existing methods in identification accuracy.

Reduces computational time significantly.

Effective in both constrained and unconstrained environments.

Abstract

In this paper we 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 distribution described by a tailored loss function. The second describes the error image as having a specific structure (resulting in low-rank in comparison to image size). 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 of our 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 face databases (in constrained and unconstrained environments) 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 implementations of the F-LR-IRNNLS and F-IRNNLS (fast version of the RRC) : https://github.com/miliadis/FIRC