Skip to main content
SpringerLink
Log in

A real-time framework for eye detection and tracking

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Epidemiological studies indicate that automobile drivers from varying demographics are confronted by difficult driving contexts such as negotiating intersections, yielding, merging and overtaking. We aim to detect and track the face and eyes of the driver during several driving scenarios, allowing for further understanding of a driver’s visual search pattern behavior. Traditionally, detection and tracking of objects in visual media has been performed using specific techniques. These techniques vary in terms of their robustness and computational cost. This research proposes a real-time framework that is built upon a foundation synonymous to boosting, which we extend from learners to trackers and demonstrate that the idea of an integrated framework employing multiple trackers is advantageous in forming a globally strong tracking methodology. In order to model the effectiveness of trackers, a confidence parameter is introduced to help minimize the errors produced by incorrect matches and allow more effective trackers with a higher confidence value to correct the perceived position of the target.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Baldock, M.R.J., Mathias, J.L., McLean, A.J., Berndt, A.: Self-regulation of driving and its relationship to driving ability among older adults. Accid. Anal. Prev. 38, 1038–1045 (2006)

    Article  Google Scholar 

  2. Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 4, 532–540 (1983)

    Article  Google Scholar 

  3. Cootes, T.F.: Images with annotations of a talking face. http://www.isbe.man.ac.uk/∼bim/data/talking_face/talking_face.html, 4 November 2007

  4. Cootes, T.F., Cooper, D., Taylor, C.J., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)

    Article  Google Scholar 

  5. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. Conf. Comput. Vis. 2, 484–498 (1998)

    Google Scholar 

  6. Cristinacce, D., Cootes, T.: Facial feature detection using AdaBoost with shape constraints. In: Proceedings of the British Machine Vision Conference, pp. 231–240 (2003)

  7. Cristinacce, D., Cootes, T.: A comparison of shape constrained facial feature detectors. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, pp. 375–380 (2004)

  8. Cristinacce, D., Cootes, T.: Feature detection and tracking with constrained local models. In: Proceedings of the British Machine Vision Conference, pp. 929–938 (2006)

  9. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp. 23–37 (1995)

  10. Abu Ghrabieh, R., Hamarneh, G., Gustavsson, T.: Review—active shape models—part II: image search and classification. In: Proceedings of the Swedish Symposium on Image Analysis, pp. 129–132 (1998)

  11. Hansen, D.W., Pece, A.E.C.: Eye tracking in the wild. Comput. Vis. Image Underst. 98(1), 155–181 (2005)

    Article  Google Scholar 

  12. Kanade, T., Cohn, J.F., Yingli, T.: Comprehensive database for facial expression analysis. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, pp. 46–53 (2000)

  13. Kanaujia, A., Huang, Y., Metaxas, D.: Emblem detections by tracking facial features. In: Proceedings of the IEEE Computer Vision and Pattern Recognition, pp. 108–108 (2006)

  14. Kearns, M.: Thoughts on hypothesis boosting. Unpublished manuscript (1988)

  15. Leinhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. Proc. Int. Conf. Image Process. 1, 900–903 (2002)

    Google Scholar 

  16. Lowe, D.G.: Object recognition from local scale-invariant features. Proc. Int. Conf. Comput. Vis. 2, 1150 (1999)

    Google Scholar 

  17. Medioni, G., Kang, S.B.: Emerging Topics in Computer Vision. Prentice-Hall, Englewood Cliffs (2005)

    Google Scholar 

  18. Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–315 (1965)

    MATH  Google Scholar 

  19. Sebe, N., Lew, M.S., Cohen, I., Yafei, S., Gevers, T., Huang, T.S.: Authentic facial expression analysis. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, pp. 242–247 (2004)

  20. Tao, H., Huang, T.S.: Connected vibrations: a modal analysis approach for non-rigidmotion tracking. In: Proceedings of the Conference on IEEE Computer Vision and Pattern Recognition, pp. 735–740 (1998)

  21. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. Proc. IEEE Comput. Vis. Pattern Recognit. 1, 511–518 (2001)

    Google Scholar 

  22. Viola, P., Jones, M.: Robust real-time face detection. Int J Comput. Vis. 57, 137–154 (2004)

    Article  Google Scholar 

  23. Wang, Y., Liu, Y., Tao, L., Xu, G.: Real-time multi-view face detection and pose estimation in video stream. In: Proceedings of the Conference on Pattern Recognition, vol. 4, pp. 354–357 (2006)

  24. Zhu, Z., Ji, Q.: Robust pose invariant facial feature detection and tracking in real-time. Proc. Int. Conf. Pattern Recognit. 1, 1092–1095 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Western Ontario, 1151 Richmond Street, London, Canada

    Hussein O. Hamshari & Steven S. Beauchemin

Authors
  1. Hussein O. Hamshari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven S. Beauchemin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hussein O. Hamshari.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hamshari, H.O., Beauchemin, S.S. A real-time framework for eye detection and tracking. J Real-Time Image Proc 6, 235–245 (2011). https://doi.org/10.1007/s11554-010-0178-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-010-0178-1

Keywords

Search

Navigation