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Neural Processing Letters

, 34:177 | Cite as

Face Recognition Using Kernel UDP

  • Wankou Yang
  • Changyin Sun
  • Jingyu Yang
  • Helen S. Du
  • Karl Ricanek
Article

Abstract

UDP has been successfully applied in many fields, finding a subspace that maximizes the ratio of the nonlocal scatter to the local scatter. But UDP can not represent the nonlinear space well because it is a linear method in nature. Kernel methods can otherwise discover the nonlinear structure of the images. To improve the performance of UDP, kernel UDP (a nonlinear vision of UDP) is proposed for face feature extraction and face recognition via kernel tricks in this paper. We formulate the kernel UDP theory and develop a two-stage method to extract kernel UDP features: namely weighted Kernel PCA plus UDP. The experimental results on the FERET and ORL databases show that the proposed kernel UDP is effective.

Keywords

UDP Kernel Feature extraction Face Recognition 

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Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Wankou Yang
    • 1
  • Changyin Sun
    • 1
  • Jingyu Yang
    • 2
  • Helen S. Du
    • 3
  • Karl Ricanek
    • 4
  1. 1.School of AutomationSoutheast UniversityNanjingPeople’s Republic of China
  2. 2.School of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingPeople’s Republic of China
  3. 3.Department of ComputingThe Hong Kong Polytechnic UniversityHong KongHong Kong
  4. 4.Face Aging Group, Department of Computer ScienceUNC WilmingtonWilmingtonUSA

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