Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition
Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition
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DOI：
10.1109/tnnls.2013.2249088
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发表时间：
2013-03
影响因子：
10.4
通讯作者：
Jian Yang;D. Chu;Lei Zhang;Yong Xu;Jing-yu Yang
中科院分区：
文献类型：
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作者：
Jian Yang;D. Chu;Lei Zhang;Yong Xu;Jing-yu Yang
一个稀疏表示为基础的分类器（SRC）的开发和现实世界的人脸识别显示出巨大的潜力。本文提出了一种适合于SRC的降维方法。SRC采用了一种基于类重构残差的决策规则，我们将其作为指导特征提取方法设计的准则。因此，该方法被称为SRC转向判别投影（SRC-DP）。SRC-DP最大化投影空间中类间重建残差与类内重建残差的比率，从而使SRC能够实现更好的性能。SRC-DP提供了人脸的低维表示，使基于SRC的人脸识别系统更有效。在AR、扩展的Yale B和PIE人脸图像库上的实验结果表明，该方法比其他基于SRC的特征提取方法更有效。
A sparse representation-based classifier (SRC) is developed and shows great potential for real-world face recognition. This paper presents a dimensionality reduction method that fits SRC well. SRC adopts a class reconstruction residual-based decision rule, we use it as a criterion to steer the design of a feature extraction method. The method is thus called the SRC steered discriminative projection (SRC-DP). SRC-DP maximizes the ratio of between-class reconstruction residual to within-class reconstruction residual in the projected space and thus enables SRC to achieve better performance. SRC-DP provides low-dimensional representation of human faces to make the SRC-based face recognition system more efficient. Experiments are done on the AR, the extended Yale B, and PIE face image databases, and results demonstrate the proposed method is more effective than other feature extraction methods based on the SRC.