Supervised kernel locality preserving projections for face recognition

	Supervised kernel locality preserving projections for face recognition
	Cheng, J; Liu, QS; Lu, HQ; Chen, YW
刊名	NEUROCOMPUTING
	2005-08-01
卷号	67 页码:443-449
关键词	kernel trick subspace analysis locality preserving projection face recognition
英文摘要	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.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence
研究领域[WOS]	Computer Science
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION
收录类别	SCI
语种	英语
WOS记录号	WOS:000231436300032
公开日期	2015-12-24
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/9067]
专题	自动化研究所_09年以前成果
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
推荐引用方式 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.