Face recognition under varying illumination based on a 2D face shape model

Xudong Xie, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

149 Citations (Scopus)


This paper proposes a novel illumination compensation algorithm, which can compensate for the uneven illuminations on human faces and reconstruct face images in normal lighting conditions. A simple yet effective local contrast enhancement method, namely block-based histogram equalization (BHE), is first proposed. The resulting image processed using BHE is then compared with the original face image processed using histogram equalization (HE) to estimate the category of its light source. In our scheme, we divide the light source for a human face into 65 categories. Based on the category identified, a corresponding lighting compensation model is used to reconstruct an image that will visually be under normal illumination. In order to eliminate the influence of uneven illumination while retaining the shape information about a human face, a 2D face shape model is used. Experimental results show that, with the use of principal component analysis for face recognition, the recognition rate can be improved by 53.3% to 62.6% when our proposed algorithm for lighting compensation is used.
Original languageEnglish
Pages (from-to)221-230
Number of pages10
JournalPattern Recognition
Issue number2
Publication statusPublished - 1 Jan 2005


  • 2D face shape model
  • Block-based histogram equalization
  • Face recognition
  • Illumination compensation

ASJC Scopus subject areas

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


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