A CORRECTIVE FRAMEWORK FOR FACIAL FEATURE DETECTION AND TRACKING

Hussein O. Hamshari, Steven S. Beauchemin, Denis Laurendeau, Normand Teasdale

2008

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 processing 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 framework that is built upon a foundation synonymous to boosting. 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.

References

  1. Burt, P. and Adelson, E. (1983). The laplacian pyramid as a compact image code. In IEEE Transactions on Communications, volume 4, pages 532-540.
  2. Cootes, T. (2007). Images with annotations of a talking face. Available at: http://www.isbe.man.ac.uk/ ˜bim/data/talking_face/talking_face.html. November 4th, 2007.
  3. Cootes, T., Cooper, D., Taylor, C., and Graham, J. (1995). Active shape models - their training and application. Computer Vision and Image Understanding, 61:38- 59.
  4. Cootes, T., Edwards, G., and Taylor, C. (1998). Active appearance models. In Conf. Computer Vision, volume 2, pages 484-498.
  5. Cristinacce, D. and Cootes, T. (2003). Facial feature detection using adaboost with shape constraints. In Proc. British Machine Vision Conference, pages 231-240.
  6. Cristinacce, D. and Cootes, T. (2004). A comparison of shape constrained facial feature detectors. In Proc. Int. Conf. Automatic Face and Gesture Recognition, pages 375-380.
  7. Cristinacce, D. and Cootes, T. (2006). Feature detection and tracking with constrained local models. In Proc. British Machine Vision Conference, pages 929-938.
  8. Freund, Y. and Schapire, R. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory, pages 23-37.
  9. Ghrabieh, R. A., Hamarneh, G., and Gustavsson, T. (1998). Review - active shape models - part ii: Image search and classification. In Proc. Swedish Symposium on Image Analysis, pages 129-132.
  10. Kanaujia, A., Huang, Y., and Metaxas, D. (2006). Emblem detections by tracking facial features. In Proc. IEEE Computer Vision and Pattern Recognition, pages 108- 108.
  11. Leinhart, R. and Maydt, J. (2002). An extended set of haarlike features for rapid object detection. In Proc. Int. Conf. Image Processing, volume 1, pages 900-903.
  12. Lowe, D. (1999). Object recognition from local scaleinvariant features. In Proc. Int. Conf. Computer Vision, volume 2, page 1150.
  13. Medioni, G. and Kang, S. (2005). Emerging Topics in Computer Vision. Prentice Hall.
  14. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proc. IEEE Computer Vision and Pattern Recognition, volume 1, pages 511-518.
  15. Viola, P. and Jones, M. (2004). Robust real-time face detection. Int. J. Comput. Vision, 57:137-154.
  16. Wang, Y., Liu, Y., Tao, L., and Xu, G. (2006). Real-time multi-view face detection and pose estimation in video stream. In Conf. Pattern Recognition, volume 4, pages 354-357.
  17. Zhu, Z. and Ji, Q. (2006). Robust pose invariant facial feature detection and tracking in real-time. In Proc. Int. Conf. Pattern Recognition, volume 1, pages 1092- 1095.
Download


Paper Citation


in Harvard Style

O. Hamshari H., S. Beauchemin S., Laurendeau D. and Teasdale N. (2008). A CORRECTIVE FRAMEWORK FOR FACIAL FEATURE DETECTION AND TRACKING . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 130-138. DOI: 10.5220/0001070401300138


in Bibtex Style

@conference{visapp08,
author={Hussein O. Hamshari and Steven S. Beauchemin and Denis Laurendeau and Normand Teasdale},
title={A CORRECTIVE FRAMEWORK FOR FACIAL FEATURE DETECTION AND TRACKING},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={130-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001070401300138},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - A CORRECTIVE FRAMEWORK FOR FACIAL FEATURE DETECTION AND TRACKING
SN - 978-989-8111-21-0
AU - O. Hamshari H.
AU - S. Beauchemin S.
AU - Laurendeau D.
AU - Teasdale N.
PY - 2008
SP - 130
EP - 138
DO - 10.5220/0001070401300138