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Facial and Eye Gaze Detection

  • Kang Ryoung Park
  • Jeong Jun Lee
  • Jaihie Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)

Abstract

Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implement it with a computer vision systemsetting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye’s movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.8 cmof RMS error.

Keywords

Facial and Eye Gaze detection IR-LED based camera 

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References

  1. 1.
    A. Azarbayejani., 1993. Visually Controlled Graphics. IEEE Trans. PAMI, Vol. 15, No. 6, pp. 602–605Google Scholar
  2. 2.
    K. R. Park et al., Gaze Point Detection by Computing the 3D Positions and 3D Motions of Face, IEICE Trans. Inf.&Syst.,Vol. E.83-D, No.4, pp.884–894, Apr 2000 (http://search.ieice.org/2000/pdf/e83-d-4-884.pdf)Google Scholar
  3. 3.
    K. R. Park et al., Gaze Detection by Estimating the Depth and 3D Motions of Facial Features in Monocular Images, IEICE Trans. Fundamentals, Vol. E.82-A, No. 10, pp. 2274–2284, Oct 1999Google Scholar
  4. 4.
    K. OHMURA et al., 1989. Pointing Operation Using Detection of Face Direction froma Single View. IEICE Trans. Inf.&Syst., Vol. J72-D-II, No.9, pp. 1441–1447Google Scholar
  5. 5.
    P. Ballard et al., 1995. Controlling a Computer via Facial Aspect. IEEE Trans. on SMC, Vol. 25, No. 4, pp. 669–677Google Scholar
  6. 6.
    A. Gee et al., 1996. Fast visual tracking by temporal consensus, Image and Vision Computing. Vol. 14, pp. 105–114Google Scholar
  7. 7.
    J. Heinzmann et al., 1998. 3D Facial Pose and Gaze Point Estimation using a Robust Real-Time Tracking Paradigm. Proceedings of ICAFGR, pp. 142–147Google Scholar
  8. 8.
    T. Rikert et al., 1998. Gaze Estimation using Morphable Models. Proc. of ICAFGR, pp. 436–441Google Scholar
  9. 9.
    A. Ali-A-L et al., Man-machine interface through eyeball direction of gaze. Proc. of the Southeastern Symposiumon SystemTheory 1997, pp. 478–82Google Scholar
  10. 10.
    A. TOMONO et al., 1994. Eye Tracking Method Using an Image Pickup Apparatus. European Patent Specification-94101635Google Scholar
  11. 11.
    Seika-Tenkai-Tokushuu-Go, ATR Journal, 1996Google Scholar
  12. 13.
    Porrill-J et al., Robust and optimal use of information in stereo vision. Nature. vol. 397, no. 6714, Jan. 1999, pp. 63–6CrossRefGoogle Scholar
  13. 14.
    Varchmin-AC et al., image based recognition of gaze direction using adaptive methods. Gesture and Sign Language in Human-Computer Interaction. Int. Gesture Workshop Proc. Berlin, Germany, 1998, pp. 245–57.Google Scholar
  14. 15.
    J. Heinzmann et al., 1997. Robust real-time face tracking and gesture recognition. Proc. of the IJCAI, Vol. 2, pp. 1525–1530Google Scholar
  15. 16.
    Matsumoto-Y, et al., An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. Proc. the ICAFGR 2000. pp. 499–504Google Scholar
  16. 17.
    Newman-R et al., Real-time stereo tracking for head pose and gaze estimation. Proceedings the 4th ICAFGR 2000. pp. 122–8Google Scholar
  17. 18.
    Betke-M et al., Gaze detection via self-organizing gray-scale units. Proc. Int.Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time System1999. pp. 70–6Google Scholar
  18. 19.
    K. R. Park et al., 2000. Intelligent Process Control via Gaze Detection Technology. EAAI, Vol. 13, No. 5, pp. 577–587Google Scholar
  19. 20.
    T. BROIDA et al., 1990. Recursive 3-D Motion Estimation from a Monocular Image Sequence. IEEE Trans. Aerospace and Electronic Systems, Vol. 26, No. 4, pp. 639–656Google Scholar
  20. 21.
    T. Fukuhara et al., 1993. 3D-motion estimation of human head for model-based image coding. IEE Proc., Vol. 140, No. 1, pp. 26–35Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Kang Ryoung Park
    • 1
  • Jeong Jun Lee
    • 2
  • Jaihie Kim
    • 2
  1. 1.Digital Vision Group, Innovation CenterLG Electronics Institute of TechnologySeoulRepublic of Korea
  2. 2.Computer Vision Laboratory, Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea

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