Locality preserving fisher discriminant analysis for face recognition

Authors:
Xu Zhao;Xiaoyan Tian
Affiliations:
The Laboratory of Computer Software and Theory, Beijing University of Technology, ChaoYang District, Beijing, P.R. China;The Laboratory of Computer Software and Theory, Beijing University of Technology, ChaoYang District, Beijing, P.R. China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

Citing 4
Cited 2

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds

The Journal of Machine Learning Research
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2

Face recognition using second-order discriminant tensor subspace analysis

Neurocomputing
Supervised orthogonal discriminant subspace projects learning for face recognition

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.