Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/206
PIRA download icon_1.1View/Download Full Text
Title: Median LDA : a robust feature extraction method for face recognition
Authors: Yang, J
Zhang, DD 
Yang, JY
Issue Date: 2006
Source: 2006 IEEE International Conference on Systems, Man, and Cybernetics, October 8-11, 2006, Taipei, Taiwan, p.4208-4213
Abstract: In the existing LDA models, class mean vector is always estimated by the class sample average. In small sample size problems such as face recognition, however, the class sample average does not suffice to provide an accurate estimate of the class mean based on a few of given samples, particularly when there are outliers in the sample set. To overcome this weakness, we use the class median vector to estimate the class mean vector in LDA modeling. The class median vector has two advantages over the class sample average: (1) the class median (image) vector preserves useful details in the sample images and (2) the class median vector is robust to outliers that exist in training sample set. The proposed median LDA model is evaluated using three popular face image databases. All experiment results indicate that median LDA is more effective than the common LDA and PCA.
Keywords: Face recognition
Linear discriminant analysis
Kernak Fisher discriminant
Principal component analysis
Publisher: IEEE
ISBN: 1424401003
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Conf_V5_07.pdf2.76 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

146
Last Week
3
Last month
Citations as of Mar 24, 2024

Downloads

301
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

26
Last Week
0
Last month
0
Citations as of Mar 22, 2024

WEB OF SCIENCETM
Citations

19
Last Week
0
Last month
1
Citations as of Mar 28, 2024

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.