An efficient illumination normalization method for face recognition

Xudong Xie, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

146 Citations (Scopus)


In this paper, an efficient representation method insensitive to varying illumination is proposed for human face recognition. Theoretical analysis based on the human face model and the illumination model shows that the effects of varying lighting on a human face image can be modeled by a sequence of multiplicative and additive noises. Instead of computing these noises, which is very difficult for real applications, we aim to reduce or even remove their effect. In our method, a local normalization technique is applied to an image, which can effectively and efficiently eliminate the effect of uneven illuminations while keeping the local statistical properties of the processed image the same as in the corresponding image under normal lighting condition. After processing, the image under varying illumination will have similar pixel values to the corresponding image that is under normal lighting condition. Then, the processed images are used for face recognition. The proposed algorithm has been evaluated based on the Yale database, the AR database, the PIE database, the YaleB database and the combined database by using different face recognition methods such as PCA, ICA and Gabor wavelets. Consistent and promising results were obtained, which show that our method can effectively eliminate the effect of uneven illumination and greatly improve the recognition results.
Original languageEnglish
Pages (from-to)609-617
Number of pages9
JournalPattern Recognition Letters
Issue number6
Publication statusPublished - 15 Apr 2006


  • Face recognition
  • Gabor wavelets
  • Illumination compensation
  • Independent component analysis (ICA)
  • Local normalization method
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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