Locality Preserving Fisher Discriminant Analysis for Face Recognition

Dimensionality reduction is a key technology for face recognition. In this paper, we propose a novel method, called Locality Preserving Fisher Discriminant Analysis (LPFDA), which extends the original Fisher Discriminant Analysis by preserving the locality structure of the data. LPFDA can get a subspace projection matrix by solving a generalized eigenvalue problem. Several experiments are conducted to demonstrate the effectiveness and robustness of our method.

[1]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[2]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[3]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[4]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  I. Jolliffe.  Principal Component Analysis , 2002 .

[6]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[7]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[8]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[9]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[10]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[11]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.