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Real-Time Eye Detection and Tracking Under Various Light Conditions  

박호식 (관동대학교)
배철수 (관동대학교)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.8, no.2, 2004 , pp. 456-463 More about this Journal
Abstract
Non-intrusive methods based on active remote IR illumination for eye tracking is important for many applications of vision-based man-machine interaction. One problem that has plagued those methods is their sensitivity to lighting condition change. This tends to significantly limit their scope of application. In this paper, we present a new real-time eye detection and tacking methodology that works under variable and realistic lighting conditions. Based on combining the bright-Pupil effect resulted from IR light and the conventional appearance-based object recognition technique, our method can robustly track eyes when the pupils ale not very bright due to significant external illumination interferences. The appearance model is incorporated in both eyes detection and tacking via the use of support vector machine and the mean shift tracking. Additional improvement is achieved from modifying the image acquisition apparatus including the illuminator and the camera.
Keywords
Eye Detection; Eye Tracking; Support Vector Machine; Human Computer Interaction;
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1 Haro, A., Flickner, M., AND Essa, I. 'Detecting and tracking eyes by using their physiological properties, dynamics, and appearance.' In Proceedings IEEE CVPR 2000. 2000
2 Morimoto, C., Koons, D., Amir, A., and Flickner, M. 'Pupil detection and tracking using multiple light sources.' Technical Report RJ-10117, IBM Almaden Research Center. 1998
3 TURK, M., AND PENTLAND, A. 'Eigenfaces for recognition.' Journal of Cognitive Neuroscience 3, 1, 71-86. 1991   DOI   ScienceOn
4 VAPNIK, V. 'The nature of statistical learning theory.' Springer-Verlag, New York. 1995
5 Fitzgibbon, A. W., and Fisher, R. 'A buyers guide to conic fitting.' In Proc. 5th British Machine Vision Conference, 513-522. 1995
6 Cortec, C., AND Vapnik, V. 'Support-vector networks.' Machine Learning 20, 273-297. 1995
7 Ebisawa, Y., and Satoh, S. 'Effectiveness of pupil area detection technique using two light sources and image difference method.' In Proceedings of the 15th Annual Int. Conf. of the IEEE Eng. in Medicine and Biology Society, 1268-1269. 1993
8 Oliver, N., Pentland, A., and Berard, F. 'Lips and face real time tracker.' In Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, 123-129. 1997
9 Comaniciu, D., Ramesh, V., and Meer, P. 'Real-time tracking of non-rigid objects using mean shift.' In IEEE Conf. on Comp. Vis. and Pat. Rec. 2000
10 Baluja, S., and Pomerleau, D. 'Non-intrusive gaze tracking using artificial neural networks.' Technical Report CMU-CS-94-102, Carnegie Mellon University. 1994
11 SMITH, P., SHAH, M., AND LOBO, N. D. V. 'Monitoring head/eye motion for driver alertness with one camera.' In Proceedings of the 2000 International Conference on Pattern Recognition,Session P4.3A. 2000
12 Ji, Q., and Yang, X. 'Real time visual cues extraction for monitoring driver vigilance.' In Proc. of International Workshop on Computer Vision Systems. 2001
13 Morimoto C., and Flickner, M. 'Real-time multiple face detection using active illumination.' In Proc. of the 4th IEEE International Conference on Automatic Face and Gesture Recognition 2000. 2000
14 Huang, J., II, D., Shao, X., and Wexhsler, H. 'Pose discrimination and eye detection using support vector machines (svms).' In Proceeding of NATO-ASI on Face Recognition: From Theory to Applications, 528-536. 1998