Feature selection via simultaneous sparse approximation for person specific face verification
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.
Cite
@article{arxiv.1102.2743,
title = {Feature selection via simultaneous sparse approximation for person specific face verification},
author = {Yixiong Liang and Lei Wang and Shenghui Liao and Beiji Zou},
journal= {arXiv preprint arXiv:1102.2743},
year = {2011}
}