CASIA OpenIR  > 09年以前成果
Supervised kernel locality preserving projections for face recognition
Cheng, J; Liu, QS; Lu, HQ; Chen, YW
Source PublicationNEUROCOMPUTING
2005-08-01
Volume67Pages:443-449
SubtypeArticle
AbstractSubspace 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.
KeywordKernel Trick Subspace Analysis Locality Preserving Projection Face Recognition
WOS HeadingsScience & Technology ; Technology
WOS KeywordNONLINEAR DIMENSIONALITY REDUCTION
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000231436300032
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9067
Collection09年以前成果
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
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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