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	Learning a locality preserving subspace for visual recognition
	He, Xiaofei ; Yan, Shuicheng ; Hu, Yuxiao ; Zhang, Hong-Jiang
	2003
英文摘要	Previous works have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analysis (PCA) and linear discriminant analysis (LDA) are considered effective in deriving such a face subspace. However, both of them effectively see only the Euclidean structure of face space. In this paper, we propose a new approach to mapping face images into a sub-space obtained by Locality Preserving Projections (LPP) for face analysis. We call this Laplacianface approach. Different from PCA and LDA, LPP finds an embedding that preserves local information, and obtains a face space that best detects the essential manifold structure. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. We compare the proposed Laplacianface approach with eigenface and fisherface methods on three test datasets. Experimental results show that the proposed Laplacianface approach provides a better representation and achieves tower error rates in face recognition.; EI; 0
语种	英语
出处	EI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/329294]
专题	数学科学学院
推荐引用方式 GB/T 7714	He, Xiaofei,Yan, Shuicheng,Hu, Yuxiao,et al. Learning a locality preserving subspace for visual recognition. 2003-01-01.

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