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ExCuSe: Robust Pupil Detection in Real-World Scenarios

  • Wolfgang FuhlEmail author
  • Thomas Kübler
  • Katrin Sippel
  • Wolfgang Rosenstiel
  • Enkelejda Kasneci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)

Abstract

The reliable estimation of the pupil position is one the most important prerequisites in gaze-based HMI applications. Despite the rich landscape of image-based methods for pupil extraction, tracking the pupil in real-world images is highly challenging due to variations in the environment (e.g. changing illumination conditions, reflection, etc.), in the eye physiology or due to variations related to further sources of noise (e.g., contact lenses or mascara). We present a novel algorithm for robust pupil detection in real-world scenarios, which is based on edge filtering and oriented histograms calculated via the Angular Integral Projection Function. The evaluation on over 38,000 new, hand-labeled eye images from real-world tasks and 600 images from related work showed an outstanding robustness of our algorithm in comparison to the state-of-the-art. Download link (algorithm and data): https://www.ti.uni-tuebingen.de/Pupil-detection.1827.0.html?&L=1.

Keywords

Edge Image Edge Pixel White Point Pupil Center Threshold Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 476–480 (1999)CrossRefGoogle Scholar
  3. 3.
    Goni, S., Echeto, J., Villanueva, A., Cabeza, R.: Robust algorithm for pupil-glint vector detection in a video-oculography eyetracking system. In: Pattern Recognition. ICPR 2004, vol. 4, pp. 941–944. IEEE (2004)Google Scholar
  4. 4.
    Kasneci, E.: Towards the Automated Recognition of Assistance Need for Drivers with Impaired Visual Field. Ph.D. thesis, University of Tübingen, Wilhelmstr. 32, 72074 Tübingen (2013)Google Scholar
  5. 5.
    Kasneci, E., Sippel, K., Aehling, K., Heister, M., Rosenstiel, W., Schiefer, U., Papageorgiou, E.: Driving with Binocular Visual Field Loss? A Study on a Supervised On-road Parcours with Simultaneous Eye and Head Tracking. Plos One (2014). doi: 10.1371/journal.pone.0087470
  6. 6.
    Keil, A., Albuquerque, G., Berger, K., Magnor, M.A.: Real-time gaze tracking with a consumer-grade video cameraGoogle Scholar
  7. 7.
    Li, D., Winfield, D., Parkhurst, D.J.: Starburst: a hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops, pp. 79–79. IEEE (2005)Google Scholar
  8. 8.
    Lin, L., Pan, L., Wei, L., Yu, L.: A robust and accurate detection of pupil images. In: 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI), vol. 1, pp. 70–74. IEEE (2010)Google Scholar
  9. 9.
    Liu, X., Xu, F., Fujimura, K.: Real-time eye detection and tracking for driver observation under various light conditions. In: IEEE Intelligent Vehicle Symposium, 2002, vol. 2, pp. 344–351. IEEE (2002)Google Scholar
  10. 10.
    Long, X., Tonguz, O.K., Kiderman, A.: A high speed eye tracking system with robust pupil center estimation algorithm. In: 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society. EMBS 2007, pp. 3331–3334. IEEE (2007)Google Scholar
  11. 11.
    Mohammed, G.J., Hong, B.R., Jarjes, A.A.: Accurate pupil features extraction based on new projection function. Computing and Informatics 29(4), 663–680 (2012)Google Scholar
  12. 12.
    Peréz, A., Cordoba, M., Garcia, A., Méndez, R., Munoz, M., Pedraza, J.L., Sanchez, F.: A precise eye-gaze detection and tracking systemGoogle Scholar
  13. 13.
    Schnipke, S.K., Todd, M.W.: Trials and tribulations of using an eye-tracking system. In: CHI 2000 extended abstracts on Human factors in computing systems, pp. 273–274. ACM (2000)Google Scholar
  14. 14.
    Sippel, K., Kasneci, E., Aehling, K., Heister, M., Rosenstiel, W., Schiefer, U., Papageorgiou, E.: Binocular Glaucomatous Visual Field Loss and Its Impact on Visual Exploration - A Supermarket Study. PLoS ONE 9(8), e106089 (2014)CrossRefGoogle Scholar
  15. 15.
    Świrski, L., Bulling, A., Dodgson, N.: Robust real-time pupil tracking in highly off-axis images. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 173–176. ACM (2012)Google Scholar
  16. 16.
    Tafaj, E., Kasneci, G., Rosenstiel, W., Bogdan, M.: Bayesian online clustering of eye movement data. In: Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA 2012, pp. 285–288. ACM (2012)Google Scholar
  17. 17.
    Tafaj, E., Kübler, T.C., Kasneci, G., Rosenstiel, W., Bogdan, M.: Online classification of eye tracking data for automated analysis of traffic hazard perception. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds.) ICANN 2013. LNCS, vol. 8131, pp. 442–450. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  18. 18.
    Tafaj, E., Kübler, T., Peter, J., Schiefer, U., Bogdan, M., Rosenstiel, W.: Vishnoo - an open-source software for vision research. In: Proceedings of the \(24^{th}\) IEEE International Symposium on Computer-Based Medical Systems, CBMS 2011, pp. 1–6. IEEE (2011)Google Scholar
  19. 19.
    Valenti, R., Gevers, T.: Accurate eye center location through invariant isocentric patterns. Transactions on pattern analysis and machine intelligence 34(9), 1785–1798 (2012)CrossRefGoogle Scholar
  20. 20.
    Yuen, H., Illingworth, J., Kittler, J. Ellipse detection using the hough transform. In: Alvey Vision Conference, pp. 1–8 (1988)Google Scholar
  21. 21.
    Zhu, D., Moore, S.T., Raphan, T.: Robust pupil center detection using a curvature algorithm. Computer methods and programs in biomedicine 59(3), 145–157 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wolfgang Fuhl
    • 1
    Email author
  • Thomas Kübler
    • 1
  • Katrin Sippel
    • 1
  • Wolfgang Rosenstiel
    • 1
  • Enkelejda Kasneci
    • 1
  1. 1.Eberhard Karls Universität TübingenTübingenGermany

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