Feature selection via simultaneous sparse approximation for person specific face verification

TL;DR

This paper introduces a novel multi-task feature selection method using simultaneous sparse approximation for person-specific face verification, improving the selection of discriminant features for compact and effective face recognition models.

Contribution

It reformulates person-specific feature selection as a simultaneous sparse approximation problem, applying sparsity-enforced regularization for the first time in face verification.

Findings

Effective feature selection on LFW database

Improved face verification accuracy

Compact person-specific models

Abstract

There is an increasing use of some imperceivable and redundant local features for face recognition. While only a relatively small fraction of them is relevant to the final recognition task, the feature selection is a crucial and necessary step to select the most discriminant ones to obtain a compact face representation. In this paper, we investigate the sparsity-enforced regularization-based feature selection methods and propose a multi-task feature selection method for building person specific models for face verification. We assume that the person specific models share a common subset of features and novelly reformulated the common subset selection problem as a simultaneous sparse approximation problem. To the best of our knowledge, it is the first time to apply the sparsity-enforced regularization methods for person specific face verification. The effectiveness of the proposed methods is verified with the challenging LFW face databases.