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Supervised kernel locality preserving projections for face recognition
Cheng, J; Liu, QS; Lu, HQ; Chen, YW
2005-08-01
发表期刊NEUROCOMPUTING
卷号67页码:443-449
文章类型Article
摘要Subspace analysis is an effective approach for face recognition. Finding a suitable low-dimensional subspace is a key step of subspace analysis, for it has a direct effect on recognition performance. In this paper, a novel subspace method, named supervised kernel locality preserving projections (SKLPP), is proposed for face recognition, in which geometric relations are preserved according to prior class-label information and complex nonlinear variations of real face images are represented by nonlinear kernel mapping. SKLPP cannot only gain a perfect approximation of face manifold, but also enhance local within-class relations. Experimental results show that the proposed method can improve face recognition performance. (c) 2005 Elsevier B.V. All rights reserved.
关键词Kernel Trick Subspace Analysis Locality Preserving Projection Face Recognition
WOS标题词Science & Technology ; Technology
关键词[WOS]NONLINEAR DIMENSIONALITY REDUCTION
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000231436300032
引用统计
被引频次:62[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9067
专题09年以前成果
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
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Cheng, J,Liu, QS,Lu, HQ,et al. Supervised kernel locality preserving projections for face recognition[J]. NEUROCOMPUTING,2005,67:443-449.
APA Cheng, J,Liu, QS,Lu, HQ,&Chen, YW.(2005).Supervised kernel locality preserving projections for face recognition.NEUROCOMPUTING,67,443-449.
MLA Cheng, J,et al."Supervised kernel locality preserving projections for face recognition".NEUROCOMPUTING 67(2005):443-449.
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