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Title:Using Graph Model for Face Analysis
Author(s):Cai, Deng; He, Xiaofei; Han, Jiawei
Subject(s):computer science
Abstract:Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modelled by low dimensional linear spaces. The typical methods for learning a face subspace include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projection (LPP). Theoretical analysis shows that all these three methods can be obtained from different graph models which correspond to different geometrical structures. In this paper, we systematically analyze the relationship between these three subspace methods. We shows that LPP provides a more general framework for subspace learning and a natural solution to the small sample issue in LDA. Extensive experiments on face recognition and clustering are performed on Yale, ORL and PIE databases.
Issue Date:2005-09
Genre:Technical Report
Other Identifier(s):UIUCDCS-R-2005-2636
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-20

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