Kernel Fisher LPP for Face Recognition

  • Yu-jie Zheng
  • Jing-yu Yang
  • Jian Yang
  • Xiao-jun Wu
  • Wei-dong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


Subspace analysis is an effective approach for face recognition. Locality Preserving Projections (LPP) finds an embedding subspace that preserves local structure information, and obtains a subspace that best detects the essential manifold structure. Though LPP has been applied in many fields, it has limitations to solve recognition problem. In this paper, a novel subspace method, called Kernel Fisher Locality Preserving Projections (KFLPP), is proposed for face recognition. In our method, discriminant information with intrinsic geometric relations is preserved in subspace in term of Fisher criterion. Furthermore, complex nonlinear variations of face images, such as illumination, expression, and pose, are represented by nonlinear kernel mapping. Experi-mental results on ORL and Yale database show that the proposed method can improve face recognition performance.


Face Recognition Linear Discriminant Analysis Face Image Locally Linear Embedding Discriminant Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yu-jie Zheng
    • 1
  • Jing-yu Yang
    • 1
  • Jian Yang
    • 2
  • Xiao-jun Wu
    • 3
  • Wei-dong Wang
    • 1
  1. 1.Department of Computer ScienceNanjing University of Science and TechnologyNanjingP. R. China
  2. 2.Department of ComputingHong Kong Polytechnic UniversityKowloon, Hong Kong
  3. 3.School of Electronics and InformationJiangsu University of Science and TechnologyZhenjiangP.R.China

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