Minimization of influence by the drooping eyelid in extracting pupil center for eye tracking systems

Abstract Precise pupil center detection is an important factor for gaze tracking in video-oculography (VOG) systems. Existing methods perform well to extract the features when the area of pupil in eye image is clear, whereas, interferences, such as eyelashes, corneal reflection etc., will lead to a low success rate. One main reason is the closure of eyelids. In this paper, a systemic 3D transformation algorithm is proposed to accurately ascertain the pupil center, in spite of the complicating factors mentioned above. Experiments show the good performance of our method. And the pupil center could be extracted accurately, even though only 25% of the pupil is visible.

[1]  L. Young,et al.  Survey of eye movement recording methods , 1975 .

[2]  Carlos Hitoshi Morimoto,et al.  Eye gaze tracking techniques for interactive applications , 2005, Comput. Vis. Image Underst..

[3]  Moshe Eizenman,et al.  General theory of remote gaze estimation using the pupil center and corneal reflections , 2006, IEEE Transactions on Biomedical Engineering.

[4]  Myung Jin Chung,et al.  A novel non-intrusive eye gaze estimation using cross-ratio under large head motion , 2005, Comput. Vis. Image Underst..

[5]  Rafael Cabeza,et al.  Eye tracking: Pupil orientation geometrical modeling , 2006, Image Vis. Comput..

[6]  Zhi-Hua Zhou,et al.  Projection functions for eye detection , 2004, Pattern Recognit..

[7]  Jie Yang,et al.  A robust method for eye features extraction on color image , 2005, Pattern Recognit. Lett..

[8]  Kwangjae Sung,et al.  Analysis Of Two Video Eye Tracking Algorithms , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[9]  Mehdi Hatamian,et al.  Design Considerations for a Real-Time Ocular Counterroll Instrument , 1983, IEEE Transactions on Biomedical Engineering.

[10]  M. A. Frens,et al.  Recording eye movements with video-oculography and scleral search coils: a direct comparison of two methods , 2002, Journal of Neuroscience Methods.

[11]  Lu Xu,et al.  An Accurate and Fast Iris Location Method Based on the Features of Human Eyes , 2005, FSKD.

[12]  S T Moore,et al.  Robust pupil center detection using a curvature algorithm. , 1999, Computer methods and programs in biomedicine.

[13]  Kyung-Mo Park,et al.  An image processing method for improved pupil size estimation accuracy , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[14]  J.H. Kim,et al.  A Fast Center of Pupil Detection Algorithm for VOG-Based Eye Movement Tracking , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[15]  Yoshinobu Ebisawa,et al.  Improved video-based eye-gaze detection method , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[16]  Steven T. Moore,et al.  A geometric basis for measurement of three-dimensional eye position using image processing , 1996, Vision Research.

[17]  Thomas Haslwanter,et al.  Improving calibration of 3-D video oculography systems , 2004, IEEE Transactions on Biomedical Engineering.

[18]  Deok Won Kim,et al.  A new method for accurate and fast measurement of 3D eye movements. , 2006, Medical engineering & physics.

[19]  Daniela Iacoviello,et al.  Parametric characterization of the form of the human pupil from blurred noisy images , 2005, Comput. Methods Programs Biomed..