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文章基本信息

  • 标题:Eye Detection in Facial Images Using Zernike Moments with SVM
  • 本地全文:下载
  • 作者:Kim, Hyoung-Joon ; Kim, Whoi-Yul
  • 期刊名称:ETRI Journal
  • 印刷版ISSN:1225-6463
  • 电子版ISSN:2233-7326
  • 出版年度:2008
  • 卷号:30
  • 期号:2
  • 页码:335-337
  • 语种:English
  • 出版社:Electronics and Telecommunications Research Institute
  • 摘要:An eye detection method for facial images using Zernike moments with a support vector machine (SVM) is proposed. Eye/non-eye patterns are represented in terms of the magnitude of Zernike moments and then classified by the SVM. Due to the rotation-invariant characteristics of the magnitude of Zernike moments, the method is robust against rotation, which is demonstrated using rotated images from the ORL database. Experiments with TV drama videos showed that the proposed method achieved a 94.6% detection rate, which is a higher performance level than that achievable by the method that uses gray values with an SVM.
  • 关键词:Eye detection;Zernike moments;support vector machine (SVM)
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