Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction

TL;DR

This paper introduces a novel hierarchical sparse and low-rank regression method utilizing gradient direction features to improve occluded face recognition, demonstrating superior performance over existing techniques.

Contribution

It proposes a new weak low-rankness optimization framework combined with gradient features for robust occluded face recognition, outperforming current state-of-the-art methods.

Findings

Outperforms existing methods on real-world occlusion data

Effective in handling contiguous face occlusions

Enhances recognition accuracy significantly

Abstract

In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed. It utilizes the robust image gradient direction features together with a variety of mapping functions and adopts a hierarchical sparse and low-rank regression model. This model unites the sparse representation in dictionary learning and the low-rank representation on the error term that is usually messy in the gradient domain. We call it the "weak low-rankness" optimization problem, which can be efficiently solved by the framework of Alternating Direction Method of Multipliers (ADMM). The optimum of the error term has a similar weak low-rank structure as the reference error map and the recognition performance can be enhanced by leaps and bounds using weak low-rankness optimization. Extensive experiments are conducted on real-world disguise / occlusion data and synthesized contiguous occlusion data. These experiments show that the proposed gradient direction-based hierarchical adaptive sparse and low-rank (GD-HASLR) algorithm has the best performance compared to state-of-the-art methods, including popular convolutional neural network-based methods.