Fast Eye Tracking and Feature Measurement using a Multi-stage Particle Filter

Radu Danescu, Adrian Sergiu Darabant, Diana Borza

Abstract

Eye trackers – systems that measure the activity of the eyes – are nowadays used in creative ways into a variety of domains: medicine, psychology, automotive industry, marketing etc. This paper presents a real time method for tracking and measuring eye features (iris position, eye contour, blinks) in video frames based on particle filters. We propose a coarse-to-fine approach to solve the eye tracking problem: a first particle filter is used to roughly estimate the position of the iris centers. Next, this estimate is analysed to decide the state of the eyes: opened or half-opened/closed. If the eyes are opened, two independent particles filters are used to determine the contour of each eye. Our algorithm takes less than 11 milliseconds on a regular PC.

References

  1. Borza, D., Darabant, A. S., and Danescu, R. (2016). Realtime detection and measurement of eye features from color images. Sensors, 16(7):1105.
  2. Campos, R., Santos, C., and Sequeira, J. (2013). Eye tracking system using particle filters. In IEEE 3rd Portuguese Meeting in Bioengineering, 1-4.
  3. Cormen, T. H., Stein, C., Rivest, R. L., and Leiserson, C. E. (2001). Introduction to Algorithms. McGraw-Hill Higher Education, 2nd edition.
  4. Cristinacce, D. and Cootes, T. (2008). Automatic feature localisation with constrained local models. Pattern Recogn., 41(10):3054-3067.
  5. Daugman, J. (2002). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14:21-30.
  6. Hansen D.W. and Q. Ji. (2010). In the Eye of the Beholder: A Survey of Models for Eyes and Gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3): 478-500.
  7. Isard, M. and Blake, A. (1998). Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision, 29:5-28.
  8. Kanade, T., Cohn, J. F., and Tian, Y. (2000). Comprehensive database for facial expression analysis. In 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 46- 53.
  9. Li, Y., Wang, S., and Ding, X. (2010). Eye/eyes tracking based on a unified deformable template and particle filtering. Pattern Recognition Letters, 31(11):1377 - 1387.
  10. Loy, G. and A. Zelinsky. (2003). Fast Radial Symmetry for Detecting Points of Interest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8): 959- 973.
  11. Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression. In 3rd Workshop on CVPR for Human Communicative Behavior Analysis, San Francisco, USA, 94-101.
  12. Moore, R. and Lopes, J. (1999). Paper templates. In TEMPLATE' 06, 1st International Conference on Template Production. SCITEPRESS.
  13. Morimoto, C. (2000). Pupil detection and tracking using multiple light sources. Image and Vision Computing, 18(4):331-335.
  14. Sirohey, S. A. and Rosenfeld, A. (2001). Eye detection in a face image using linear and nonlinear filters. Pattern Recognition, 34(7):1367-1391.
  15. Smith, J. (1998). The Book. The publishing company, London, 2nd edition.
  16. Wu, J. and Trivedi, M. M. (2008). Simultaneous eye tracking and blink detection with interactive particle filters. EURASIP J. Adv. Sig. Proc., 2008.
  17. Wu, J. and Trivedi, M. M. (2010). An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation. ACM Trans. Multimedia Comput. Commun. Appl., 6(2):8:1-8:23.
  18. Yuille, A. L., Hallinan, P.W., and Cohen, D. S. (1992). Feature extraction from faces using deformable templates. Int. J. Comput. Vision, 8(2):99-111.
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Paper Citation


in Harvard Style

Danescu R., Sergiu Darabant A. and Borza D. (2017). Fast Eye Tracking and Feature Measurement using a Multi-stage Particle Filter . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 258-265. DOI: 10.5220/0006130202580265


in Bibtex Style

@conference{visapp17,
author={Radu Danescu and Adrian Sergiu Darabant and Diana Borza},
title={Fast Eye Tracking and Feature Measurement using a Multi-stage Particle Filter},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={258-265},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006130202580265},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Fast Eye Tracking and Feature Measurement using a Multi-stage Particle Filter
SN - 978-989-758-226-4
AU - Danescu R.
AU - Sergiu Darabant A.
AU - Borza D.
PY - 2017
SP - 258
EP - 265
DO - 10.5220/0006130202580265