Face recognition based on eigen-illumination scheme and uncorrelated discriminant analysis

Danghui Liu, Lansun Shen, Kin Man Lam

Research output: Journal article publicationConference articleAcademic researchpeer-review


The illumination changes on face images make face recognition a very difficult task. In this paper, a human face representation scheme that is insensitive to illumination variation is proposed in order to deal with the problem. The variations in lighting over human faces are modeled by means of Principal Component Analysis (PCA) on a number of blurred faces under different lighting conditions. Then the 'difference image', which is the difference between the original image and the reconstructed image, is used for face recognition. We also propose an uncorrelated Linear Discriminant Analysis technique for face recognition based on the eigen-illumination representation scheme. This method can obtain the uncorrelated optimal discriminant vectors (UODVs) so that the extracted features are uncorrelated. Experimental results show that the proposed method is effective to deal with varying illumination problem for face recognition.
Original languageEnglish
Pages (from-to)829-836
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue number2
Publication statusPublished - 1 Dec 2005
EventVisual Communications and Image Processing 2005 - Beijing, China
Duration: 12 Jul 200515 Jul 2005


  • Eigen-illumination
  • Face Recognition
  • Principal Component Analysis
  • Uncorrelated Discriminant Analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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