Orthogonal laplacianfaces for face recognition

Deng Cai, Xiaofei He, Jiawei Han, Hong Jiang Zhang

Research output: Contribution to journalArticlepeer-review


Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our proposed algorithm.

Original languageEnglish (US)
Pages (from-to)3608-3614
Number of pages7
JournalIEEE Transactions on Image Processing
Issue number11
StatePublished - Nov 2006


  • Appearance-based vision
  • Face recognition
  • Locality preserving projection (LPP)
  • Orthogonal locality preserving projection (OLPP)

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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