Locality-Regularized Linear Regression for Face Recognition

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Brown, Douglas
Li, Hanxi
Gao, Yongsheng
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IAPR
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2012
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Abstract

Linear Regression Classification (LRC) based face recognition achieves high accuracy while being highly efficient. As with most other linear-subspace-based methods, the faces of a subject are assumed to reside on a linear manifold; however, where occlusion or disturbances are involved, this assumption may be inaccurate. In this paper, a manifold-learning procedure is used to expand on conventional LRC by excluding faces not fitting the original assumption (of linearity), thereby localizing the manifold subspace, increasing the accuracy over conventional LRC and reducing the number of faces for which the regression must be performed. The algorithm is evaluated using two standard databases and shown to outperform the conventional LRC.

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2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)
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© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Computer vision
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