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

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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

57 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)1736-1747
頁數12
期刊Pattern Recognition
43
發行號5
DOIs
出版狀態已發佈 - 2010 五月

ASJC Scopus subject areas

  • 軟體
  • 訊號處理
  • 電腦視覺和模式識別
  • 人工智慧

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