Pupil detection and tracking using multiple light sources

Abstract We present a fast, robust, and low cost pupil detection technique that uses two near-infrared time multiplexed light sources synchronized with the camera frame rate. The two light sources generate bright and dark pupil images, which are used for pupil segmentation. To reduce artifacts caused mostly by head motion, a larger temporal support is used. This method can be applied to detect and track several pupils (or several people). Experimental results from a real-time implementation of the system show that this technique is very robust, and able to detect pupils using wide field of view low cost cameras under different illumination conditions, even for people with glasses, from considerable long distances.

[1]  Thomas S. Huang,et al.  Face detection with information-based maximum discrimination , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  S. Birchfield,et al.  An elliptical head tracker , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

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

[4]  H D Crane,et al.  Accurate two-dimensional eye tracker using first and fourth Purkinje images. , 1973, Journal of the Optical Society of America.

[5]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Alex Pentland,et al.  LAFTER: lips and face real time tracker , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Alexander H. Waibel,et al.  A real-time face tracker , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[9]  Worthy N. Martin,et al.  Human-computer interaction using eye-gaze input , 1989, IEEE Trans. Syst. Man Cybern..

[10]  Yukio Kobayashi,et al.  A TV Camera System Which Extracts Feature Points For Non-Contact Eye Movement Detection , 1990, Other Conferences.

[11]  Trevor Darrell,et al.  Active face tracking and pose estimation in an interactive room , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Yoshinobu Ebisawa,et al.  Unconstrained Pupil Detection TechniqueUsing Two Light Sources And The ImageDifference Method , 1970 .

[13]  Yoshinobu Ebisawa,et al.  Effectiveness of pupil area detection technique using two light sources and image difference method , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[14]  Kiyoharu Aizawa,et al.  Detection and tracking of facial features , 1995, Other Conferences.

[15]  Roberto Cipolla,et al.  Fast visual tracking by temporal consensus , 1996, Image Vis. Comput..

[16]  Paul W. Fieguth,et al.  Color-based tracking of heads and other mobile objects at video frame rates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  H. D. Crane,et al.  Accurate three-dimensional eyetracker. , 1978, Applied optics.

[18]  Alex Waibel,et al.  A model-based gaze tracking system , 1996, Proceedings IEEE International Joint Symposia on Intelligence and Systems.

[19]  Ravi Kothari,et al.  Detection of eye locations in unconstrained visual images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.