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Journal of Electronic Imaging

Sparsity preserving discriminative learning with applications to face recognition
Author(s): Yingchun Ren; Zhicheng Wang; Yufei Chen; Xiaoying Shan; Weidong Zhao
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Paper Abstract

The extraction of effective features is extremely important for understanding the intrinsic structure hidden in high-dimensional data. In recent years, sparse representation models have been widely used in feature extraction. A supervised learning method, called sparsity preserving discriminative learning (SPDL), is proposed. SPDL, which attempts to preserve the sparse representation structure of the data and simultaneously maximize the between-class separability, can be regarded as a combiner of manifold learning and sparse representation. More specifically, SPDL first creates a concatenated dictionary by class-wise principal component analysis decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least squares method. Second, a local between-class separability function is defined to characterize the scatter of the samples in the different submanifolds. Then, SPDL integrates the learned sparse representation information with the local between-class relationship to construct a discriminant function. Finally, the proposed method is transformed into a generalized eigenvalue problem. Extensive experimental results on several popular face databases demonstrate the effectiveness of the proposed approach.

Paper Details

Date Published: 11 January 2016
PDF: 13 pages
J. Electron. Imaging. 25(1) 013005 doi: 10.1117/1.JEI.25.1.013005
Published in: Journal of Electronic Imaging Volume 25, Issue 1
Show Author Affiliations
Yingchun Ren, Tongji Univ. (China)
Jiaxing Univ. (China)
Zhicheng Wang, Tongji Univ. (China)
Yufei Chen, Tongji Univ. (China)
Xiaoying Shan, Jiaxing Univ. (China)
Weidong Zhao, Tongji Univ. (China)


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