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Title:Face Recognition via Sparse Representation
Author(s):You, Jason
Contributor(s):Ma, Yi
Subject(s):automatic face recognition
sparse representation
L1 minimization
Abstract:We study the problem of automatically face recognition under varying illumination and poses. We show how techniques for representing sparse signals can be used to effectively handle the difficulties due to lighting and disguise. The idea is to cast the problem as a sparse representation problem, and solve the problem by using the L1-minimization method. The desired representation is sparse, since the test face image should only be represented in terms of training face images of the same object. Our new algorithm uses the L1 minimization method to express the test image as a sparse linear combination of the all training images plus a sparse error due to corruption or occlusion. We conduct experiments on publicly available databases to verify the efficacy of the proposed method. Besides the algorithm, we also design and build our own hardware equipment and user interface to acquire training images.
Issue Date:2008-05
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Date Available in IDEALS:2014-01-27

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