Regularized Robust Coding for Face Recognition

TL;DR

This paper introduces a new face recognition coding model called regularized robust coding (RRC), which improves robustness and efficiency over traditional sparse representation methods, especially under challenging conditions like occlusion and lighting changes.

Contribution

The paper proposes the RRC model and an IR3C algorithm, offering a more effective and computationally efficient approach for face recognition compared to existing sparse coding methods.

Findings

RRC outperforms state-of-the-art methods in accuracy under occlusion and lighting variations.

IR3C algorithm efficiently solves the RRC model with lower computational cost.

Extensive experiments validate the robustness and effectiveness of RRC in real-world scenarios.

Abstract

Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes SRC's computational cost very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting and expression changes, etc.