Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/219
Title: Globally maximizing, locally minimizing : unsupervised discriminant projection with applications to face and palm biometrics
Authors: Yang, J
Zhang, DD 
Yang, JY
Niu, B
Keywords: Dimensionality reduction
Feature extraction
Subspace learning
Fisher linear discriminant analysis (LDA)
Manifold learning
Biometrics
Face recognition
Palmprint recognition
Issue Date: Apr-2007
Publisher: IEEE Computer Society
Source: IEEE transactions on pattern analysis and machine intelligence, Apr. 2007, v. 29, no. 4, p. 650-664 How to cite?
Journal: IEEE transactions on pattern analysis and machine intelligence 
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.
URI: http://hdl.handle.net/10397/219
ISSN: 0162-8828
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.
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