Discriminant Sparsity Preserving Analysis for Face Recognition
Discriminant Sparsity Preserving Analysis for Face Recognition
复制标题
人脸识别的判别稀疏性保持分析
DOI：
10.1142/s0218001416560036
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发表时间：
2016-02
影响因子：
1.5
通讯作者：
Lili Hou
中科院分区：
文献类型：
--
作者：
Ying Wen;Le Zhang;Lili Hou
Sparse subspace learning has drawn more and more attentions recently, however, most of them are unsupervised and unsuitable for classification tasks. In this paper, a new discriminant sparsity preserving analysis (DSPA) method by integrating sparse reconstructive weighting into Fisher criterion is proposed for face recognition. We first get sparsity preserving space spanned by the eigenvectors of sparsity preserving projections (SPP). Then, the optimal projection can be obtained by solving an eigenvalue and eigenvector problem of the between-class scatter matrix in sparsity preserving space. The method not only preserves the sparse reconstructive relationship of the data, but also encodes the discriminant information. Extensive experiments on four face image datasets (Yale, ORL, AR and CMU PIE) demonstrate the effectiveness of the proposed DSPA method.
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影响因子：
10.6
作者：
Cheng, Bin;Yang, Jianchao;Huang, Thomas S.
通讯作者：
Huang, Thomas S.
DOI：
10.1002/047134608x.w5513.pub2
发表时间：
2019-02
期刊：
Wiley Encyclopedia of Electrical and Electronics Engineering
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
Lorenzo Vaquero;V. Brea;M. Mucientes
DOI：
10.1049/iet-bmt.2018.5117
发表时间：
2016
期刊：
IET Biom.
影响因子：
--
作者：
Sheng He;Lambert Schomaker
通讯作者：
Sheng He;Lambert Schomaker
影响因子：
8
作者：
Gui, Jie;Sun, Zhenan;Ji, Shuiwang
通讯作者：
Ji, Shuiwang