Geometric Eye Gaze Tracking

Adam Strupczewski, Błażej Czupryński, Jacek Naruniec, Kamil Mucha

Abstract

This paper presents a novel eye gaze estimation method based on calculating the gaze vector in a geometric approach. There have been many publications in the topic of eye gaze estimation, but most are related to using dedicated infra red equipment and corneal glints. The presented approach, on the other hand, assumes that only an RGB input image of the user’s face is available. Furthermore, it requires no calibration but only simple one-frame initialization. In comparison to other systems presented in literature, our method has better accuracy. The presented method relies on determining the 3D location of the face and eyes in the initialization frame, tracking these locations in each consecutive frame and using this knowledge to estimating the gaze vector and point where the user is looking. The algorithm runs in real time on mobile devices.

References

  1. Cootes, T. F., Edwards, G. J., and Taylor, C. J. (2001). Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell., 23(6):681-685.
  2. Czuprynski, B. and Strupczewski, A. (2014). High accuracy head pose tracking survey. In Active Media Technology, volume 8610 of Lecture Notes in Computer Science, pages 407-420. Springer International Publishing.
  3. Daugman, J. (2002). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14:21-30.
  4. Dodgson, N. A. (2004). Variation and extrema of human interpupillary distance.
  5. Goss, D. A. and West, R. (2002). Introduction to the optics of the eye. Butterworth-Heinemann.
  6. Hansen, D. W. and Ji, Q. (2010). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell., 32(3):478-500.
  7. Hennessey, C., Noureddin, B., and Lawrence, P. (2006). A single camera eye-gaze tracking system with free head motion. pages 87-94.
  8. Ishikawa, T., Baker, S., Matthews, I., and Kanade, T. (2004). Passive Driver Gaze Tracking with Active Appearance Models. Technical Report CMU-RI-TR-04- 08, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
  9. Jang, J. and Kanade, T. (2004). Robust 3d head tracking by online feature registration.
  10. Jang, J.-S. and Kanade, T. (2010). Robust 3d head tracking by view-based feature point registration. Available at www.consortium.ri.cmu.edu/projCylTrack.php.
  11. Kim, J.-T. and Kim, D. (2007). Gaze Tracking with Active Appearance Models. Technical report, Department of Computer Science and Engineering, Pohang University of Science and Technology.
  12. Leutenegger, S., Chli, M., and Siegwart, R. Y. (2011). Brisk: Binary robust invariant scalable keypoints. In Proceedings of the 2011 International Conference on Computer Vision, ICCV 7811, pages 2548-2555, Washington, DC, USA. IEEE Computer Society.
  13. Liao, W.-K., Fidaleo, D., and Medioni, G. (2010). Robust: Real-time 3d face tracking from a monocular view. J. Image Video Process., 2010:5:1-5:15.
  14. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vision, 60(2):91- 110.
  15. Lu, F., Okabe, T., Sugano, Y., and Sato, Y. (2014). Learning gaze biases with head motion for head pose-free gaze estimation. Image and Vision Computing, 32(3):169 - 179.
  16. Matthews, I. and Baker, S. (2004). Active appearance models revisited. Int. J. Comput. Vision, 60(2):135-164.
  17. Morency, L., Whitehill, J., and Movellan, J. (2008). Generalized adaptive view-based appearance model: Integrated framework for monocular head pose estimation. pages 1-8.
  18. Ohno, T., Mukawa, N., and Yoshikawa, A. (2002). Freegaze: A gaze tracking system for everyday gaze interaction. pages 125-132.
  19. Riordan-Eva, P., Whitcher, J., Vaughan, D., and Asbury, T. (2004). Vaughan & Asbury's General Ophthalmology. A Lange medical book. Lange Medical Books/McGraw Hill Medical Pub. Division.
  20. Vacchetti, L., Lepetit, V., and Fua, P. (2004). Stable realtime 3d tracking using online and offline information. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(10):1385-1391.
  21. Valenti, R., Sebe, N., and Gevers, T. (2012). Combining head pose and eye location information for gaze estimation. Image Processing, IEEE Transactions on, 21(2):802-815.
  22. Valenti, R., Staiano, J., Sebe, N., and Gevers, T. (2009). Webcam-based visual gaze estimation. In Foggia, P., Sansone, C., and Vento, M., editors, Image Analysis and Processing - ICIAP 2009, volume 5716 of Lecture Notes in Computer Science, pages 662-671. Springer Berlin Heidelberg.
  23. Viola, P. and Jones, M. J. (2004). Robust real-time face detection. Int. J. Comput. Vision, 57(2):137-154.
  24. Wood, E. and Bulling, A. (2014). Eyetab: Model-based gaze estimation on unmodified tablet computers. In Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA 7814, pages 207-210, New York, NY, USA. ACM.
  25. Xiao, J., Baker, S., Matthews, I., and Kanade, T. (2004). Real-time combined 2d+3d active appearance models. pages 535-542.
  26. Xiao, J., Kanade, T., and Cohn, J. F. (2002). Robust fullmotion recovery of head by dynamic templates and re-registration techniques.
  27. Zhang, W., Li, B., Ye, X., and Zhuang, Z. (2007). A robust algorithm for iris localization based on radial symmetry. pages 324-327.
  28. Zhou, Z., Yao, P., Zhuang, Z., and Li, J. (2011). A robust algorithm for iris localization based on radial symmetry and circular integro differential operator. pages 1742- 1745.
Download


Paper Citation


in Harvard Style

Strupczewski A., Czupryński B., Naruniec J. and Mucha K. (2016). Geometric Eye Gaze Tracking . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 444-455. DOI: 10.5220/0005676304440455


in Bibtex Style

@conference{visapp16,
author={Adam Strupczewski and Błażej Czupryński and Jacek Naruniec and Kamil Mucha},
title={Geometric Eye Gaze Tracking},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={444-455},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005676304440455},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Geometric Eye Gaze Tracking
SN - 978-989-758-175-5
AU - Strupczewski A.
AU - Czupryński B.
AU - Naruniec J.
AU - Mucha K.
PY - 2016
SP - 444
EP - 455
DO - 10.5220/0005676304440455