Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/219
PIRA download icon_1.1View/Download Full Text
Title: Globally maximizing, locally minimizing : unsupervised discriminant projection with applications to face and palm biometrics
Authors: Yang, J
Zhang, DD 
Yang, JY
Niu, B
Issue Date: Apr-2007
Source: IEEE transactions on pattern analysis and machine intelligence, Apr. 2007, v. 29, no. 4, p. 650-664
Abstract: This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, Locality Preserving Projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications.
Keywords: Dimensionality reduction
Feature extraction
Subspace learning
Fisher linear discriminant analysis (LDA)
Manifold learning
Biometrics
Face recognition
Palmprint recognition
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on pattern analysis and machine intelligence 
ISSN: 0162-8828
EISSN: 1939-3539
DOI: 10.1109/TPAMI.2007.1008
Rights: © 2007 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:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
264.pdf2.38 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

128
Last Week
3
Last month
Citations as of May 15, 2022

Downloads

823
Citations as of May 15, 2022

SCOPUSTM   
Citations

465
Last Week
2
Last month
5
Citations as of May 19, 2022

WEB OF SCIENCETM
Citations

381
Last Week
1
Last month
7
Citations as of May 12, 2022

Google ScholarTM

Check

Altmetric


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