Discriminative Local Sparse Representations for Robust Face Recognition

TL;DR

This paper introduces a robust local sparsity-based face recognition method that overcomes alignment issues by analyzing local features and their dependencies, significantly improving recognition accuracy under various distortions.

Contribution

It proposes a novel local block-based sparsity model with a probabilistic graphical framework to handle misalignment and local variations in face recognition.

Findings

Effective against registration errors like translation, rotation, and scaling

Improves recognition rates under pose and illumination variations

Outperforms existing methods on benchmark face databases

Abstract

A key recent advance in face recognition models a test face image as a sparse linear combination of a set of training face images. The resulting sparse representations have been shown to possess robustness against a variety of distortions like random pixel corruption, occlusion and disguise. This approach however makes the restrictive (in many scenarios) assumption that test faces must be perfectly aligned (or registered) to the training data prior to classification. In this paper, we propose a simple yet robust local block-based sparsity model, using adaptively-constructed dictionaries from local features in the training data, to overcome this misalignment problem. Our approach is inspired by human perception: we analyze a series of local discriminative features and combine them to arrive at the final classification decision. We propose a probabilistic graphical model framework to explicitly mine the conditional dependencies between these distinct sparse local features. In particular, we learn discriminative graphs on sparse representations obtained from distinct local slices of a face. Conditional correlations between these sparse features are first discovered (in the training phase), and subsequently exploited to bring about significant improvements in recognition rates. Experimental results obtained on benchmark face databases demonstrate the effectiveness of the proposed algorithms in the presence of multiple registration errors (such as translation, rotation, and scaling) as well as under variations of pose and illumination.