ACCURATE EYE CENTRE LOCALISATION BY MEANS OF GRADIENTS

Fabian Timm, Erhardt Barth

2011

Abstract

The estimation of the eye centres is used in several computer vision applications such as face recognition or eye tracking. Especially for the latter, systems that are remote and rely on available light have become very popular and several methods for accurate eye centre localisation have been proposed. Nevertheless, these methods often fail to accurately estimate the eye centres in difficult scenarios, e.g. low resolution, low contrast, or occlusions. We therefore propose an approach for accurate and robust eye centre localisation by using image gradients. We derive a simple objective function, which only consists of dot products. The maximum of this function corresponds to the location where most gradient vectors intersect and thus to the eye’s centre. Although simple, our method is invariant to changes in scale, pose, contrast and variations in illumination. We extensively evaluate our method on the very challenging BioID database for eye centre and iris localisation. Moreover, we compare our method with a wide range of state of the art methods and demonstrate that our method yields a significant improvement regarding both accuracy and robustness.

References

  1. Asadifard, M. and Shanbezadeh, J. (2010). Automatic adaptive center of pupil detection using face detection and cdf analysis. In Proceedings of the IMECS, volume I, pages 130-133, Hong Kong. Newswood Limited.
  2. Asteriadis, S., Asteriadis, S., Nikolaidis, N., Hajdu, A., and Pitas, I. (2006). An eye detection algorithm using pixel to edge information. In Proceedings of the 2nd ISCCSP, Marrakech, Morocco. EURASIP.
  3. Behnke, S. (2002). Learning face localization using hierarchical recurrent networks. In Proceedings of the ICANN, LNCS, pages 135-135. Springer.
  4. Böhme, M., Meyer, A., Martinetz, T., and Barth, E. (2006). Remote eye tracking: State of the art and directions for future development. In Proceedings of the 2nd COGAIN, pages 10-15, Turin, Italy.
  5. Campadelli, P., Lanzarotti, R., and Lipori, G. (2006). Precise eye localization through a general-to-specific model definition. In Proceedings of the 17th BMVC, volume I, pages 187-196, Edingburgh, England.
  6. Chen, D., Tang, X., Ou, Z., and Xi, N. (2006). A hierarchical floatboost and mlp classifier for mobile phone embedded eye location system. In Proceedings of the 3rd ISNN, LNCS, pages 20-25, China. Springer.
  7. Cristinacce, D., Cootes, T., and Scott, I. (2004). A multistage approach to facial feature detection. In Proceedings of the 15th BMVC, pages 277-286, England.
  8. Hamouz, M., Kittler, J., Kamarainen, J., Paalanen, P., Kälviäinen, H., and Matas, J. (2005). Feature-based affine-invariant localization of faces. IEEE Transactions on PAMI, 27(9):1490.
  9. Hansen, D. and Ji, Q. (2010). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. on PAMI, 32(3):478-500.
  10. Jesorsky, O., Kirchberg, K., and Frischholz, R. (2001). Robust face detection using the Hausdorff distance. In Proceedings of the 3rd AVBPA, LNCS, pages 90-95, Halmstad, Sweden. Springer.
  11. Kothari, R. and Mitchell, J. (1996). Detection of eye locations in unconstrained visual images. In Proceedings of the IEEE ICIP, volume 3, pages 519-522. IEEE.
  12. Kroon, B., Hanjalic, A., and Maas, S. (2008). Eye localization for face matching: is it always useful and under what conditions? In Proceedings of the 2008 CIVR, pages 379-388, Ontario, Canada. ACM.
  13. Niu, Z., Shan, S., Yan, S., Chen, X., and Gao, W. (2006). 2d cascaded adaboost for eye localization. In Proceedings of the 18th IEEE ICPR, volume 2, pages 1216- 1219, Hong Kong. IEEE.
  14. Türkan, M., Pardàs, M., and C¸ etin, A. E. (2007). Human eye localization using edge projections. In Proceedings of the 2nd VISAPP, pages 410-415. INSTICC.
  15. Valenti, R. and Gevers, T. (2008). Accurate eye center location and tracking using isophote curvature. In Proceedings of the CVPR, pages 1-8, Alaska. IEEE.
  16. Viola, P. and Jones, M. (2004). Robust real-time face detection. IJCV, 57(2):137-154.
  17. Zhou, Z. and Geng, X. (2004). Projection functions for eye detection. Pattern Recognition, 37(5):1049-1056.
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Paper Citation


in Harvard Style

Timm F. and Barth E. (2011). ACCURATE EYE CENTRE LOCALISATION BY MEANS OF GRADIENTS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 125-130. DOI: 10.5220/0003326101250130


in Bibtex Style

@conference{visapp11,
author={Fabian Timm and Erhardt Barth},
title={ACCURATE EYE CENTRE LOCALISATION BY MEANS OF GRADIENTS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={125-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003326101250130},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - ACCURATE EYE CENTRE LOCALISATION BY MEANS OF GRADIENTS
SN - 978-989-8425-47-8
AU - Timm F.
AU - Barth E.
PY - 2011
SP - 125
EP - 130
DO - 10.5220/0003326101250130