Supervised sparsity preserving projections for face recognition
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 withinclass 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).
Sparsity Preserving Projections (SPP) Supervised SPP (SSPP) Face recognition Sparse Representation (SR)
Yanfeng Sun Jiangang Zhao Yongli Hu
College of Computer Science and Technology, Beijing University of Technology, Beijing, China
国际会议
Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)
成都
英文
459-463
2011-04-15（万方平台首次上网日期，不代表论文的发表时间）