Abstract

We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.

Keywords

EigenfacePattern recognition (psychology)Artificial intelligenceFacial recognition systemPrincipal component analysisLinear discriminant analysisSubspace topologyComputer scienceFace (sociological concept)Nonlinear dimensionality reductionFace hallucinationMathematicsComputer visionDimensionality reductionFace detection

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Publication Info

Year
2005
Type
article
Volume
27
Issue
3
Pages
328-340
Citations
3273
Access
Closed

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Xiaofei He, Shuicheng Yan, Yuxiao Hu et al. (2005). Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence , 27 (3) , 328-340. https://doi.org/10.1109/tpami.2005.55

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DOI
10.1109/tpami.2005.55