DocumentCode :
1220991
Title :
Face recognition using Laplacianfaces
Author :
He, Xiaofei ; Yan, Shuicheng ; Hu, Yuxiao ; Niyogi, Partha ; Zhang, Hong-Jiang
Author_Institution :
Dept. of Comput. Sci., Chicago Univ., IL, USA
Volume :
27
Issue :
3
fYear :
2005
fDate :
3/1/2005 12:00:00 AM
Firstpage :
328
Lastpage :
340
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 :
approximation theory; eigenvalues and eigenfunctions; face recognition; graph theory; image representation; image sampling; principal component analysis; Eigenface method; Euclidean structure; Fisherface method; Laplace Beltrami operator; Laplacianface method; PCA; appearance based face recognition; eigenfunctions; error rate reduction; face data sets; face images; face manifold structure detection; face subspace analysis; facial expression; graph models; image sampling; linear discriminant analysis; locality preserving projections; optimal linear approximations; principal component analysis; Eigenvalues and eigenfunctions; Error analysis; Face detection; Face recognition; Helium; Image analysis; Linear approximation; Linear discriminant analysis; Principal component analysis; Training data; Index Terms- Face recognition; face manifold; linear discriminant analysis; locality preserving projections; principal component analysis; subspace learning.; Algorithms; Artificial Intelligence; Biometry; Computer Simulation; Discriminant Analysis; Face; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Linear Models; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Photography; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity; Statistical Distributions;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2005.55
Filename :
1388260
Link To Document :
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