Gaze Detection by Wide and Narrow View Stereo Camera

  • Kang Ryoung Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


Human gaze is very important information for the interaction with the computer. In this paper, we propose the new gaze detection system with a wide and a narrow view stereo camera. In order to locate the user’s eye position accurately, the narrow-view camera has the functionalities of auto P/T/Z/F based on the detected 3D eye positions from the wide view camera. In addition, we use the IR-LED illuminators for wide and narrow view camera, which can ease the detecting of facial features, pupil and iris position. The eye gaze position on a monitor is computed by a multi-layered perceptron with a limited logarithm function. Experimental results show that the gaze detection error between the computed positions and the real ones is about 2.89 cm of RMS error.


Gaze Detection Dual Cameras Dual IR-LED Illuminators 


  1. 1.
    Wang, J., Sung, E.: Study on Eye Gaze Estimation. IEEE Trans. on SMC 32(3), 332–350 (2002)Google Scholar
  2. 2.
    Azarbayejani, A.: Visually Controlled Graphics. IEEE Trans. PAMI 15(6), 602–605 (1993)Google Scholar
  3. 3.
    Park, K.R., et al.: Gaze Point Detection by Computing the 3D Positions and 3D Motions of Face. IEICE Trans. Inf. & Syst. E.83-D(4), 884–894 (2000)Google Scholar
  4. 4.
    Park, K.R., et al.: Gaze Detection by Estimating the Depth and 3D Motions of Facial Features in Monocular Images. IEICE Trans. Fundamentals E.82-A(10), 2274–2284 (1999)Google Scholar
  5. 5.
    Ohmura, K., et al.: Pointing Operation Using Detection of Face Direction from a Single View. IEICE Trans. Inf. & Syst. J72-D-II(9), 1441–1447 (1989)Google Scholar
  6. 6.
    Ballard, P., et al.: Controlling a Computer via Facial Aspect. IEEE Trans. on SMC 25(4), 669–677 (1995)Google Scholar
  7. 7.
    Gee, A., et al.: Fast visual tracking by temporal consensus. Image and Vision Computing 14, 105–114 (1996)CrossRefGoogle Scholar
  8. 8.
    Heinzmann, J., et al.: 3D Facial Pose and Gaze Point Estimation using a Robust Real-Time Tracking Paradigm. In: Proceedings of ICAFGR, pp. 142–147 (1998)Google Scholar
  9. 9.
    Rikert, T.: Gaze Estimation using Morphable Models, pp. 436–441 (1998)Google Scholar
  10. 10.
    Ali-A-L, A., et al.: Man-machine Interface through Eyeball Direction of Gaze. In: Proc. of the Southeastern Symposium on System Theory, pp. 478–482 (1997)Google Scholar
  11. 11.
    Tomono, A., et al.: Eye Tracking Method Using an Image Pickup Apparatus. In: European Patent Specification-94101635 (1994)Google Scholar
  12. 12.
    Eyemark Recorder Model EMR-NC, NAC Image Technology Cooperation Google Scholar
  13. 13.
    Porrill, J., et al.: Robust and Optimal Use of Information in Stereo Vision. Nature 397(6714), 63–66 (1999)CrossRefGoogle Scholar
  14. 14.
    Varchmin, A.C., et al.: Image based Recognition of Gaze Direction Using Adaptive Methods. Gesture and Sign Language in Human-Computer Interaction. In: Int. Gesture Workshop Proc. Berlin, Germany, pp. 245–257 (1998)Google Scholar
  15. 15.
    Heinzmann, J., et al.: Robust Real-time Face Tracking and Gesture Recognition. In: Proc. of the IJCAI, vol. 2, pp. 1525–1530 (1997)Google Scholar
  16. 16.
    Matsumoto, Y., et al.: An Algorithm for Real-time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement. In: Proc. the ICAFGR, pp. 499–504 (2000)Google Scholar
  17. 17.
    Newman, R., et al.: Real-time Stereo Tracking for Head Pose and Gaze Estimation. In: Proceedings the 4th ICAFGR 2000, pp. 122–128 (2000)Google Scholar
  18. 18.
    Betke, M., et al.: Gaze Detection via Self-organizing Gray-scale Units. In: Proc. Int. Workshop on Recog., Analy., and Tracking of Faces and Gestures in Real-Time System, pp. 70–76 (1999)Google Scholar
  19. 19.
    Park, K.R., et al.: Intelligent Process Control via Gaze Detection Technology. EAAI 13(5), 577–587 (2000)Google Scholar
  20. 20.
    Park, K.R., et al.: Gaze Position Detection by Computing the 3 Dimensional Facial Positions and Motions. Pattern Recognition 35(11), 2559–2569 (2002)zbMATHCrossRefGoogle Scholar
  21. 21.
    Park, K.R., et al.: Facial and Eye Gaze detection. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 368–376. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Yang, J., Waibel, A.: A Real-time Face Tracker. In: Proceedings of WACV 1996, pp. 142–147 (1996)Google Scholar
  23. 23.
    Matsumoto, Y.: An Algorithm for Real-time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement. In: ICFGR, pp. 499–505 (2000)Google Scholar
  24. 24.
  25. 25.
  26. 26.
    Wolfe, B., Eichmann, D.: A Neural Network Approach to Tracking Eye Position. International Journal Human Computer Interaction 9(1), 59–79 (1997)CrossRefGoogle Scholar
  27. 27.
    Beymer, D., Flickner, M.: Eye Gaze Tracking Using an Active Stereo Head. IEEE Computer Vision and Pattern Recognition (2003)Google Scholar
  28. 28.
    Zhu, J., et al.: Subpixel Eye Gaze Tracking. In: International Conference on Face and Gesture Recognition (2002)Google Scholar
  29. 29.
    Stiefelhagen, R., Yang, J., Waibel, A.: Tracking Eyes and Monitoring Eye Gaze. In: Proceedings of Workshop on Perceptual User Interfaces, pp. 98–100 (1997)Google Scholar
  30. 30.
    Daugman, J.: The Importance of Being Random: Statistical Principles of Iris Recognition. Pattern Recognition 36(2), 279–291 (2003)CrossRefGoogle Scholar
  31. 31.
    Jain, R.: Machine Vision, McGraw-Hill International edn. (1995)Google Scholar
  32. 32.

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kang Ryoung Park
    • 1
  1. 1.Division of Media TechnologySangMyung UniversitySeoulRepublic of Korea

Personalised recommendations