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A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios

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Pattern Recognition and Image Analysis (IbPRIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9117))

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Abstract

We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system.

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References

  1. NUIA eyeCharm (2014). https://www.kickstarter.com/projects/4tiitoo/nuia-eyecharm-kinect-to-eye-tracking

  2. The Eye Tribe (2014). http://theeyetribe.com/

  3. Baluja, S., Pomerleau, D.: Non-intrusive gaze tracking using artificial neural networks. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) NIPS, pp. 753–760. Morgan Kaufmann (1993)

    Google Scholar 

  4. Chen, J., Ji, Q.: 3D gaze estimation with a single camera without IR illumination. In: ICPR, pp. 1–4. IEEE (2008)

    Google Scholar 

  5. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)

    Article  Google Scholar 

  6. Duchowski, A.T.: Eye Tracking Methodology - Theory And Practice. Springer, London (2003)

    Book  MATH  Google Scholar 

  7. Ferhat, O., Vilariño, F., Sánchez, F.J.: A cheap portable eye-tracker solution for common setups. J. Eye Mov. Res. 7(3), 1–10 (2014)

    Google Scholar 

  8. Hansen, D.W., Hansen, J.P., Nielsen, M., Johansen, A.S., Stegmann, M.B.: Eye typing using Markov and active appearance models. In: WACV, pp. 132–136. IEEE Computer Society (2002)

    Google Scholar 

  9. Hansen, D.W., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010)

    Article  Google Scholar 

  10. Holland, C., Komogortsev, O.V.: Eye tracking on unmodified common tablets: challenges and solutions. In: Morimoto, C.H., Istance, H.O., Spencer, S.N., Mulligan, J.B., Qvarfordt, P. (eds.) ETRA, pp. 277–280. ACM, New York (2012)

    Chapter  Google Scholar 

  11. Ishikawa, T., Baker, S., Matthews, I., Kanade, T.: Passive driver gaze tracking with active appearance models. In: Proceedings of the 11th World Congress on Intelligent Transportation Systems, vol. 3 (2004)

    Google Scholar 

  12. Lu, F., Okabe, T., Sugano, Y., Sato, Y.: A head pose-free approach for appearance-based gaze estimation. In: Proceedings of the British Machine Vision Conference, pp. 126.1–126.11. BMVA Press (2011). doi:10.5244/C.25.126

  13. Lu, F., Sugano, Y., Okabe, T., Sato, Y.: Inferring human gaze from appearance via adaptive linear regression. In: Metaxas, D.N., Quan, L., Sanfeliu, A., Gool, L.J.V. (eds.) ICCV, pp. 153–160. IEEE (2011)

    Google Scholar 

  14. Santana, M.C., Dniz-Surez, O., Hernndez-Sosa, D., Lorenzo, J.: A comparison of face and facial feature detectors based on the viola-jones general object detection framework. Mach. Vis. Appl. 22(3), 481–494 (2011)

    Google Scholar 

  15. Sugano, Y., Matsushita, Y., Sato, Y.: Appearance-based gaze estimation using visual saliency. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 329–341 (2013)

    Article  Google Scholar 

  16. Sugano, Y., Matsushita, Y., Sato, Y., Koike, H.: An incremental learning method for unconstrained gaze estimation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 656–667. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. Valenti, R., Sebe, N., Gevers, T.: Combining head pose and eye location information for gaze estimation. IEEE Trans. Image Process. 21(2), 802–815 (2012)

    Article  MathSciNet  Google Scholar 

  18. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii (2001)

    Google Scholar 

  19. Wang, J.G., Sung, E.: Gaze determination via images of irises. In: Mirmehdi, M., Thomas, B.T. (eds.) BMVC: British Machine Vision Association (2000)

    Google Scholar 

  20. Wu, H., Chen, Q., Wada, T.: Conic-based algorithm for visual line estimation from one image. In: FGR, pp. 260–265. IEEE Computer Society (2004)

    Google Scholar 

  21. Xu, L.Q., Machin, D., Sheppard, P.: A novel approach to real-time non-intrusive gaze finding. In: Carter, J.N., Nixon, M.S. (eds.) BMVC: British Machine Vision Association (1998)

    Google Scholar 

  22. Yamazoe, H., Utsumi, A., Yonezawa, T., Abe, S.: Remote gaze estimation with a single camera based on facial-feature tracking without special calibration actions. In: ETRA, pp. 245–250. ACM (2008)

    Google Scholar 

  23. Zielinski, P.: Opengazer: open-source gaze tracker for ordinary webcams (software) (2013). http://www.inference.phy.cam.ac.uk/opengazer/

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Acknowledgements

This work was supported in part by Universitat Autònoma de Barcelona PIF grants and Google Research Awards.

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Authors and Affiliations

  1. Universitat Autònoma de Barcelona, Bellaterra, Spain

    Onur Ferhat, Arcadi Llanza & Fernando Vilariño

  2. Computer Vision Center, Bellaterra, Spain

    Onur Ferhat, Arcadi Llanza & Fernando Vilariño

Authors
  1. Onur Ferhat
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  2. Arcadi Llanza
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  3. Fernando Vilariño
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Corresponding author

Correspondence to Onur Ferhat .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de València, València, Spain

    Roberto Paredes

  2. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  3. Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Xosé M. Pardo

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Ferhat, O., Llanza, A., Vilariño, F. (2015). A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_64

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  • DOI: https://doi.org/10.1007/978-3-319-19390-8_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19389-2

  • Online ISBN: 978-3-319-19390-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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