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Feature extraction using kernel inverse FDA

  • Southeast University, Nanjing
  • Hohai University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper presents a new feature extraction method called kernel inverse Fisher discriminant analysis for face recognition. In the method, the nonlinear kernel trick is first applied to map the input data into an implicit feature space. Then the inverse Fisher discriminant analysis is used to analyze the data for producing nonlinear discriminating features Experimental results on ORL face database show that the proposed method is effective in classifying.

Original languageEnglish
Title of host publicationProceedings of the 31st Chinese Control Conference, CCC 2012
Pages3672-3675
Number of pages4
StatePublished - 2012
Externally publishedYes
Event31st Chinese Control Conference, CCC 2012 - Hefei, China
Duration: 25 Jul 201227 Jul 2012

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference31st Chinese Control Conference, CCC 2012
Country/TerritoryChina
CityHefei
Period25/07/1227/07/12

Keywords

  • Face recognition
  • Feature extraction
  • Kernel Inverse FDA
  • Kernel PCA

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