Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Real-Time Eye Detection and Tracking Under Various Light Conditions

다양한 조명하에서 실시간 눈 검출 및 추적

  • Published : 2004.04.01
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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.

본 논문에서는 다양한 조명하에서 실시간으로 눈을 검출하고 추적하는 새로운 방법을 제안하고자 한다. 기존의 능동적 적외선을 이용한 눈 검출 및 추적 방법은 외부의 조명에 매우 민감하게 반응하는 문제점을 가지고 있으므로, 본 논문에서는 적외선 조명을 이용한 밝은 동공 효과와 전형적인 외형을 기반으로 한 사물 인식 기술을 결합하여 외부 조명의 간섭으로 밝은 동공 효과가 나타나지 않는 경우에도 견실하게 눈을 검출하고 추적 할 수 있는 방법을 제안한다. 눈 검출과 추적을 위해 SVM과 평균 이동 추적방법을 사용하였고, 적외선 조명과 카메라를 포함한 영상 획득 장치를 구성하여 제안된 방법이 효율적으로 다양한 조명하에서 눈 검출과 추적을 할 수 있음을 보여 주었다.

Keywords

References

  1. Baluja, S., and Pomerleau, D. 'Non-intrusive gaze tracking using artificial neural networks.' Technical Report CMU-CS-94-102, Carnegie Mellon University. 1994
  2. 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
  3. Cortec, C., AND Vapnik, V. 'Support-vector networks.' Machine Learning 20, 273-297. 1995
  4. 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
  5. Fitzgibbon, A. W., and Fisher, R. 'A buyers guide to conic fitting.' In Proc. 5th British Machine Vision Conference, 513-522. 1995
  6. 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
  7. 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
  8. Ji, Q., and Yang, X. 'Real time visual cues extraction for monitoring driver vigilance.' In Proc. of International Workshop on Computer Vision Systems. 2001
  9. 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
  10. 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
  11. 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
  12. 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
  13. TURK, M., AND PENTLAND, A. 'Eigenfaces for recognition.' Journal of Cognitive Neuroscience 3, 1, 71-86. 1991 https://doi.org/10.1162/jocn.1991.3.1.71
  14. VAPNIK, V. 'The nature of statistical learning theory.' Springer-Verlag, New York. 1995