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Feature selection via simultaneous sparse approximation for person specific face verification

lib:99455b929d6ba2d1 (v1.0.0)

Authors: Yixiong Liang,Lei Wang,Shenghui Liao,Beiji Zou
ArXiv: 1102.02743
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1102.2743v2

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.

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