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Proceedings Paper

Supervised sparsity preserving projections for face recognition
Author(s): Yanfeng Sun; Jiangang Zhao; Yongli Hu
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Paper Abstract

Sparsity preserving projection (SPP) is a recently proposed unsupervised linear dimensionality reduction method for face recognition, which is based on the recently-emerged sparse representation theory. It aims to find a low-dimensional subspace to best preserve the global sparse reconstructive relationship of the original data. In this paper, we propose a supervised variation on SPP called supervised sparsity preserving projection (SSPP). The SSPP method explicitly takes into account the within-class weight as well as between-class weight and assigns different weights to them, which attempts to strengthen the discriminating power and generalization ability of embedded data representation. The effectiveness of the proposed SSPP method is verified on two standard face databases (Yale, AR).

Paper Details

Date Published: 8 July 2011
PDF: 5 pages
Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80092D (8 July 2011); doi: 10.1117/12.896141
Show Author Affiliations
Yanfeng Sun, Beijing Univ. of Technology (China)
Jiangang Zhao, Beijing Univ. of Technology (China)
Yongli Hu, Beijing Univ. of Technology (China)


Published in SPIE Proceedings Vol. 8009:
Third International Conference on Digital Image Processing (ICDIP 2011)
Ting Zhang, Editor(s)

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