Multi-stage iterative FLD method for face recognition

Mohammad Asmatullah Khan, Aurangzeb Khan, Tariq Mahmood, Muzahir Abbas, Khurram Saleem Alimgeer

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

Abstract

New multi-stage iterative FLD method For face recognition is proposed in this paper. In face recognition Principle Component Analysis (PCA) and Fisher Linear Discriminator (FLD) are used for recognition. Both have drawbacks like, in conventional FLD the training set is not sufficient to build a reasonable FLD basis for face recognition because of the involvement of classification information in the design process. In comparison, for PCA the training data is consider to be adequate. In order to provide reasonable training data-set for FLD basis we propose a multi-stage iterative FLD process. In each stage we consider only one, the most dominating base and move from stage to stage to build rest of basis. In this way we mitigate the effect of small training set size on the recognition performance achievable by FLD basis. The experimental results demonstrates the superiority of our approach over the existing ones.

Original languageEnglish
Title of host publication2010 International Conference on Information and Emerging Technologies, ICIET 2010
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-5
Number of pages5
ISBN (Print)9781424480012
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2nd International Conference on Information and Emerging Technologies, ICIET 2010 - Karachi, Pakistan
Duration: 14 Jun 201016 Jun 2010

Other

Other2nd International Conference on Information and Emerging Technologies, ICIET 2010
CountryPakistan
CityKarachi
Period14/06/1016/06/10

Keywords

  • Classification
  • Eigenfaces
  • Eigenvectors
  • Face recognition
  • Feature-based
  • FLD
  • PCA

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