  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.
