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2003
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Learning a Locality Preserving Subspace for Visual Recognition

9 years 8 months ago
Learning a Locality Preserving Subspace for Visual Recognition
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 subspace 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 propose...
Xiaofei He, Shuicheng Yan, Yuxiao Hu, HongJiang Zh
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2003
Where ICCV
Authors Xiaofei He, Shuicheng Yan, Yuxiao Hu, HongJiang Zhang
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