A robust eye detection method using combined binary edge and intensity information

Jiatao Song, Zheru Chi, Jilin Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

68 Citations (Scopus)

Abstract

In this paper, a new eye detection method is presented. The method consists of three steps: (1) extraction of binary edge images (BEIs) from the grayscale face image based on multi-resolution wavelet transform, (2) extraction of eye regions and segments from BEIs and (3) eye localization based on light dots and intensity information. In the paper, an improved face region extraction algorithm and a light dots detection algorithm are proposed for better eye detection performance. Also a multi-level eye detection scheme is adopted. Experimental results show that a correct eye detection rate of 98.7% can be achieved on 150 Bern images with variations in views and gaze directions and 96.6% can be achieved on 564 AR images with different facial expressions and lighting conditions.
Original languageEnglish
Pages (from-to)1110-1125
Number of pages16
JournalPattern Recognition
Volume39
Issue number6
DOIs
Publication statusPublished - 1 Jun 2006

Keywords

  • Binary edge images
  • Eye detection
  • Light dots
  • Multi-level eye detection
  • Multi-resolution face image analysis

ASJC Scopus subject areas

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

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