Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics.

Full 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.

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Author information

Author/s: Yang, Jian (J); Zhang, David (D); Yang, Jing-Yu (JY); Niu, Ben (B);

Affiliation: Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong. csjyang(-atsign-)comp.polyu.edu.hk

Journal and publication information

Publication Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2007-Apr; vol 29 (issue 4) : pp 650-64

Dates: Created 2007/02/14; Completed 2007/04/24; Revised 2008/08/28;

PMID: 17299222, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

Comments and Corrections

CommentIn: IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1503-4. (PMID: 18566503)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Biometry - methods
Computer Simulation
Discriminant Analysis
Face - anatomy & histology
Hand - anatomy & histology

Hand - anatomy & histology
Humans
Image Interpretation, Computer-Assisted - methods
Models, Biological
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity

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