Orthogonal Laplacianfaces for Face Recognition

Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the lo...

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Bibliographic Details
Published inIEEE transactions on image processing Vol. 15; no. 11; pp. 3608 - 3614
Main Authors Cai, Deng, He, Xiaofei, Han, Jiawei, Zhang, Hong-Jiang
Format Journal Article
LanguageEnglish
Published United States IEEE 01.11.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Appearance-based vision
Approximation
Approximation algorithms
Artificial Intelligence
Belts
Biometry - methods
Cluster Analysis
Eigenvalues and eigenfunctions
Face - anatomy & histology
Face detection
Face recognition
Geometry
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image reconstruction
Information Storage and Retrieval - methods
Linear approximation
Linear discriminant analysis
locality preserving projection (LPP)
Mathematical analysis
Mathematics
orthogonal locality preserving projection (OLPP)
Pattern Recognition, Automated - methods
Preserving
Principal component analysis
Projection
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Summary:Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our proposed algorithm
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2006.881945