A Discriminant Analysis Method for Face Recognition in Heteroscedastic Distributions

Authors:
Zhen Lei;Shengcai Liao;Dong Yi;Rui Qin;Stan Z. Li
Affiliations:
Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190;Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190
Venue:
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Year:
2009

Citing 9
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition

Speech Communication
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Linear Feature Extraction for Multiclass Classification Problems Based on Class Mean and Covariance Discriminant Information

IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel Mathematical Model for Enhanced Fisher's Linear Discriminant and Its Application to Face Recognition

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Approximate information discriminant analysis: A computationally simple heteroscedastic feature extraction technique

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Linear discriminant analysis (LDA) is a popular method in pattern recognition and is equivalent to Bayesian method when the sample distributions of different classes are obey to the Gaussian with the same covariance matrix. However, in real world, the distribution of data is usually far more complex and the assumption of Gaussian density with the same covariance is seldom to be met which greatly affects the performance of LDA. In this paper, we propose an effective and efficient two step LDA, called LSR-LDA, to alleviate the affection of irregular distribution to improve the result of LDA. First, the samples are normalized so that the variances of variables in each class are consistent, and a pre-transformation matrix from the original data to the normalized one is learned using least squares regression (LSR); second, conventional LDA is conducted on the normalized data to find the most discriminant projective directions. The final projection matrix is obtained by multiply the pre-transformation matrix and the projective directions of LDA. Experimental results on FERET and FRGC ver 2.0 face databases show the proposed LSR-LDA method improves the recognition accuracy over the conventional LDA by using the LSR step.