Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition

Wen Chung Kao*, Ming Chai Hsu, Yueh Yiing Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)


Recognizing human faces in various lighting conditions is quite a difficult problem. The problem becomes more difficult when face images are taken in extremely high dynamic range scenes. Most of the automatic face recognition systems assume that images are taken under well-controlled illumination. The face segmentation as well as recognition becomes much simpler under such a constrained condition. However, illumination control is not feasible when a surveillance system is installed in any location at will. Without compensating for uneven illumination, it is impossible to get a satisfactory recognition rate. In this paper, we propose an integrated system that first compensates uneven illumination through local contrast enhancement. Then the enhanced images are fed into a robust face recognition system which adaptively selects the most important features among all candidate features and performs classification by support vector machines (SVMs). The dimension of feature space as well as the selected types of features is customized for each hyperplane. Three face image databases, namely Yale, Yale Group B, and Extended Yale Group B, are used to evaluate performance. The experimental result shows that the proposed recognition system give superior results compared to recently published literatures.

Original languageEnglish
Pages (from-to)1736-1747
Number of pages12
JournalPattern Recognition
Issue number5
Publication statusPublished - 2010 May


  • Adaptive feature selection
  • Face recognition
  • Local contrast enhancement
  • Support vector machines

ASJC Scopus subject areas

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


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