Graph Regularized Sparsity Discriminant Analysis for face recognition

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• 作者：Songjiang Lou^a ; ^{lousongjiangac@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xiaoming Zhao^aAuthor Vitae ; Yuelong Chuang^aAuthor Vitae ; Haitao Yu^bAuthor Vitae ; Shiqing Zhang^aAuthor Vitae
• 关键词：Sparse representation ; Graph embedding ; Sparsity preserving projection ; Feature extraction ; Face recognition
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P2
• 页码：290-297
• 全文大小：1094 K

文摘

Manifold learning and Sparse Representation Classifier are two popular techniques for face recognition. Because manifold learning can find low-dimensional representations for high-dimensional data, it is widely applied in computer vision and pattern recognition. Most of the manifold learning algorithms can be unified in the graph embedding framework, where the first step is to determine the adjacent graphs. Traditional methods use k$k$ nearest neighbor or the ε$\epsilon$-ball schemes. However, they are parametric and sensitive to noises. Moreover, it is hard to determine the size of appropriate neighborhoods. To deal with these problems, in this paper, Graph Regularized Sparsity Discriminant Analysis, GRSDA, for short, is proposed. Based on graph embedding and sparsity preserving projection, the weight matrices for intrinsic and penalty graphs are obtained through sparse representation. GRSDA seeks a subspace in which samples in intra-classes are as compact as possible while samples in inter-classes are as separable as possible. Specifically, samples in the low-dimensional space can preserve the sparse locality relationship in the same class, while enhancing the separability for samples in different classes. Hence, GRSDA can achieve better performance. Extensive experiments were carried out on ORL, YALE-B and AR face databases, and the results confirmed that the proposed algorithm outperformed LPP, UDP, SPP and DSNPE.