Globality-locality preserving projections for biometric data dimensionality reduction

Sheng Huang, Ahmed Elgammal, Luwen Huangfu, Dan Yang, Xiaohong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

In a biometric recognition task, the manifold of data is the result of the interactions between the sub-manifold of dynamic factors of subjects and the sub-manifold of static factors of subjects. Therefore, instead of directly constructing the graph Laplacian of samples, we firstly divide each subject data into a static part (subject-invariant part) and a dynamic part (intra-subject variations) and then jointly learn their graph Laplacians to yield a new graph Laplcian. We use this new graph Laplacian to replace the original graph Laplacian of Locality Preserving Projections (LPP) to present a new supervised dimensionality reduction algorithm. We name this algorithm Globality-Locality Preserving Projections (GLPP). Moreover, we also extend GLPP into a 2D version for dimensionality reduction of 2D data. Compared to LPP, the subspace learned by GLPP more precisely preserves the manifold structures of the data and is more robust to the noisy samples. We apply it to face recognition and gait recognition. Extensive results demonstrate the superiority of GLPP in comparison with the state-of-the-art algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014
PublisherIEEE Computer Society
Pages15-20
Number of pages6
ISBN (Electronic)9781479943098, 9781479943098
DOIs
StatePublished - Sep 24 2014
Event2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

Other2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014
CountryUnited States
CityColumbus
Period6/23/146/28/14

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Keywords

  • Dimensionality Reduction
  • Face Recognition
  • Gait Recognition
  • Graph Laplacian
  • Subspace Learning

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