Eye detection in facial images using zernike moments with SVM

Hyoung Joon Kim, Whoi Yul Kim

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)335-337
Number of pages3
JournalETRI Journal
Volume30
Issue number2
DOIs
StatePublished - 2008 Apr

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Keywords

  • Eye detection
  • Support vector machine (SVM)
  • Zernike moments

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