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Eye and gaze tracking for interactive graphic display
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Source ACM International Conference Proceeding Series; Vol. 24 archive
Proceedings of the 2nd international symposium on Smart graphics table of contents
Hawthorne, New York
Pages: 79 - 85  
Year of Publication: 2002
ISBN:1-58113-555-6
Authors
Qiang Ji  Rensselaer Polytechnic Institute
Zhiwei Zhu  Univ. of Nevada, Reno
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper describes preliminary results we have obtained in developing a computer vision system based on active IR illumination for real time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using the Generalized Regression Neural Networks (GRNN). With GRNN, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. The effectiveness of our gaze tracker is demonstrated by preliminary experiments that involve gaze-contingent interactive graphic display.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
Y. Ebisawa. Unconstrained pupil detection technique using two light sources and the image difference method. Visualization and Intelligent Design in Engineering, pages 79-89, 1989.
 
3
Y. Ebisawa. Improved video-based eye-gaze detection method. IEEE Transcations on Instrumentation and Measruement, 47(2):948-955, 1998.
 
4
T. E. Hutchinson. Eye movement detection with improved calibration and speed. United States Patent {19}, (4,950,069), 1988.
 
5
T. E. Hutchinson, K. White, J. R. Worthy, N. Martin, C. Kelly, R. Lisa, , and A. Frey. Human-computer interaction using eye-gaze input. IEEE Transaction on systems,man,and cybernetics, 19(6):1527-1533, 1989.
 
6
 
7
D. Koons and M. Flickner. Ibm blue eyes project. http://www.almaden.ibm.com/cs/blueeyes.
8
 
9
R. Rae and H. Ritter. Recognition of human head orientation based on artificial neural networks. IEEE Transactions on Neural Networks, 9(2):257-265, 1998.
 
10
D. F. Specht. A general regression neural network. IEEE Transcations on Neural Networks, 2:568-576, 1991.
 
11
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