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

  • Authors:

  • Jiatao Song;Zheru Chi;Jilin Liu

  • Affiliations:

  • Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong and Institute of Information and Communication Engineering, Zhejiang Univer ...;Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, PR China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2006

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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.