Real-time eye-gaze estimation using a low-resolution webcam

  • Authors:

  • Yu-Tzu Lin;Ruei-Yan Lin;Yu-Chih Lin;Greg C. Lee

  • Affiliations:

  • Graduate Institute of Information and Computer Education, National Taiwan Normal University, Taipei, Republic of China 10610;Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Republic of China 10610;Department of Biomedical Engineering, Yuanpei University, Hsinchu, Republic of China 30015;Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Republic of China 10610

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2013

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Abstract

Eye detection and gaze estimation play an important role in many applications, e.g., the eye-controlled mouse in the assisting system for disabled or elderly persons, eye fixation and saccade in psychological analysis, or iris recognition in the security system. Traditional research usually achieves eye tracking by employing intrusive infrared-based techniques or expensive eye trackers. Nowadays, there are more and more needs to analyze user behaviors from tracking eye attention in general applications, in which users usually use a consumer-grade computer or even laptop with an inexpensive webcam. To satisfy the requirements of rapid developments of such applications and reduce the cost, it is no more practical to apply intrusive techniques or use expensive/specific equipment. In this paper, we propose a real-time eye-gaze estimation system by using a general low-resolution webcam, which can estimate eye-gaze accurately without expensive or specific equipment, and also without an intrusive detection process. An illuminance filtering approach is designed to remove the influence from light changes so that the eyes can be detected correctly from the low-resolution webcam video frames. A hybrid model combining the position criterion and an angle-based eye detection strategy are also derived to locate the eyes accurately and efficiently. In the eye-gaze estimation stage, we employ the Fourier Descriptor to describe the appearance-based features of eyes compactly. The determination of eye-gaze position is then carried out by the Support Vector Machine. The proposed algorithms have high performances with low computational complexity. The experiment results also show the feasibility of the proposed methodology.