Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

Konstantinos Papachristou, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP 2015)
Subtitle of host publicationProceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3185-3189
Number of pages5
ISBN (Electronic)9781479983391
ISBN (Print)9781479983407
DOIs
Publication statusPublished - Jan 2016
Event2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada
Duration: 27 Sep 201530 Sep 2015

Conference

Conference2015 IEEE International Conference on Image Processing (ICIP)
CountryCanada
CityQuebec City, ON
Period27/09/1530/09/15

Keywords

  • facial image analysis
  • subspace learning
  • symmetry constraint
  • two-dimensional linear discriminant analysis

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