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	Square Loss based Regularized LDA for Face Recognition Using Image Sets
	Geng, Yanlin ; Shan, Caifeng ; Hao, Pengwei
	2009
关键词	DISCRIMINANT-ANALYSIS CLASSIFICATION
英文摘要	In this paper, we focus on face recognition over image sets, where each set is represented by a linear subspace. Linear Discriminant Analysis (LDA) is adopted for discriminative learning. After investigating the relation between regularization on Fisher Criterion and Maximum Margin Criterion, we present a unified framework for regularized LDA. With the framework, the ratio-form maximization of regularized Fisher LDA can be reduced to the difference form optimization with an additional constraint. By incorporating the empirical loss as the regularization term, we introduce a generalized Square Loss based Regularized LDA (SLR-LDA) with suggestion on parameter setting. Our approach achieves superior performance to the state-of-the-art methods on face recognition. Its effectiveness is also evidently verified in general object and object category recognition experiments.; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1109/CVPR.2009.5204307
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/260956]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Geng, Yanlin,Shan, Caifeng,Hao, Pengwei. Square Loss based Regularized LDA for Face Recognition Using Image Sets. 2009-01-01.

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