Supervised sparsity preserving projections for face recognition

Yanfeng Sun; Jiangang Zhao; Yongli Hu

doi:10.1117/12.896141

8 July 2011 Supervised sparsity preserving projections for face recognition

Yanfeng Sun, Jiangang Zhao, Yongli Hu

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 80092D (2011) https://doi.org/10.1117/12.896141
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

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).

Citation Download Citation

Yanfeng Sun, Jiangang Zhao, and Yongli Hu "Supervised sparsity preserving projections for face recognition", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80092D (8 July 2011); https://doi.org/10.1117/12.896141

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