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Support vector features and the role of dimensionality in face authentication
Halmstad University.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
Swiss Federal Institute of Technology, Lausanne.
2002 (English)In: Pattern recognition with support vector machines / [ed] Seong-Whan Lee, Alessandro Verri, Berlin: Springer , 2002, Vol. LNCS-2388, 249-259 p.Conference paper, Published paper (Other academic)
Abstract [en]

A study of the dimensionality of the Face Authentication problem using Principal Component Analysis (PCA) and a novel dimensionality reduction algorithm that we call Support Vector Features (SVFs) is presented. Starting from a Gabor feature space, we show that PCA and SVFs identify distinct subspaces with comparable authentication and generalisation performance. Experiments using KNN classifiers and Support Vector Machines (SVMs) on these reduced feature spaces show that the dimensionality at which saturation of the authentication performance is achieved heavily depends on the choice of the classifier. In particular, SVMs involve directions in feature space that carry little variance and therefore appear to be vulnerable to excessive PCA-based compression.

Place, publisher, year, edition, pages
Berlin: Springer , 2002. Vol. LNCS-2388, 249-259 p.
Series
Lecture notes in computer science, ISSN 0302-9743 ; 2388
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-14914ISBN: 3-540-44016-X OAI: oai:DiVA.org:hh-14914DiVA: diva2:408410
Conference
First international workshop, SVM 2002, Niagara Falls, Canada, August 10, 2002
Available from: 2011-04-04 Created: 2011-04-04 Last updated: 2011-11-14Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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