Modal Regression based Atomic Representation for Robust Face Recognition

TL;DR

This paper introduces a novel face recognition framework based on modal regression that effectively handles complex noise without assuming specific noise distributions, improving robustness over traditional methods.

Contribution

The paper proposes the MRARC framework, a new atomic representation method using modal regression, capable of managing various complex noises in face recognition tasks.

Findings

MRARC outperforms traditional RC methods under complex noise conditions.

The framework is validated on real-world data demonstrating robustness.

Four new RC methods for unimodal and multimodal face recognition are developed.

Abstract

Representation based classification (RC) methods such as sparse RC (SRC) have shown great potential in face recognition in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression based atomic representation and classification (MRARC) framework to alleviate such limitation. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal face recognition, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers (ADMM) and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust face recognition.