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Surface Recalibration as a New Method Improving Gaze-Based Human-Computer Interaction

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 722)

Abstract

The main problem of a gaze-based interaction is the correct mapping from an output of eye tracker to a gaze point. In this paper we propose a new method of improvement of the gaze-based human computer interaction using: (a) a procedure to estimate the error introduced by screen tracking algorithms (surface recalibration) and (b) using the obtained error data to transform the eye-tracking data in real-time (data transformation). In order to test the developed method, we conducted initial pilot study using simple target pointing procedure. Initial data gathered during these tests shows that our method may increase the effectiveness (measured as target pointing speed) of the gaze-based interaction using mobile eye trackers. In future studies it is worth testing this method using stationary eye trackers as it can be an effective way of facilitating gaze-based interaction by counteracting calibration errors that would yield gaze-based system unusable.

Keywords

  • Eye tracking
  • Human-Computer interaction
  • Real-time computing

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References

  1. Feit, A., Williams, S., Toledo, A., Paradiso, A., Kulkarni, H., Kane, S., Morris, M.R.: Toward everyday gaze input: accuracy and precision of eye tracking and implications for design. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 1118–1130. ACM, New York (2017)

    Google Scholar 

  2. Harezlak, K., Kasprowski, P., Stasch, M.: Towards accurate eye tracker calibration – methods and procedures. Procedia Comput. Sci. 35, 1073–1081 (2014)

    CrossRef  Google Scholar 

  3. Lander, C.: Methods for calibration free and multi-user eye tracking. In: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 899–900. ACM, New York (2016)

    Google Scholar 

  4. Sugano, Y., Bulling, A.: Self-calibrating head-mounted eye trackers using egocentric visual saliency. In: Proceedings of the 28th Annual ACM Symposium, pp. 363–372. ACM, New York (2015)

    Google Scholar 

  5. Tripathi, S., Guenter, B.: A statistical approach to continuous self-calibrating eye gaze tracking for head-mounted virtual reality systems. In: 2017 IEEE Winter Conference on Applications of Computer Vision, pp. 862–870. IEEE, Santa Rosa (2017)

    Google Scholar 

  6. Kang, S.: Multi-user identification-based eye-tracking algorithm using position estimation. Sensors 17(1), 41 (2016)

    CrossRef  Google Scholar 

  7. Hornof, A., Halverson, T.: Cleaning up systematic error in eye-tracking data by using required fixation locations. Behav. Res. Methods Instrum. Comput. 34, 592–604 (2002)

    CrossRef  Google Scholar 

  8. Forget, A., Chiasson, S., Biddle, R.: Input precision for gaze-based graphical passwords. In: CHI 2010 Extended Abstracts on Human Factors in Computing Systems, pp. 4279–4284. ACM, New York (2010)

    Google Scholar 

  9. Vadillo, M.A., Street, C.N.H., Beesley, T., Shanks, D.R.: A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation. Behav. Res. Methods 47, 1365–1376 (2015)

    CrossRef  Google Scholar 

  10. Špakov, O., Gizatdinova, Y.: Real-time hidden gaze point correction. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 291–294. ACM, New York (2014)

    Google Scholar 

  11. Kassner, M., Patera, W., Bulling, A.: Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Adjunct Publication, Seattle (2014)

    Google Scholar 

  12. Young, M.E., Crumer, A.: Reaction time. In: Vonk, J., Shackelford, T.K. (eds.) Encyclopedia of Animal Cognition and Behavior. Springer, Cham (2019, to appear)

    Google Scholar 

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Correspondence to Cezary Biele .

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Biele, C., Kobylinski, P. (2018). Surface Recalibration as a New Method Improving Gaze-Based Human-Computer Interaction. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration. IHSI 2018. Advances in Intelligent Systems and Computing, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-319-73888-8_31

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  • DOI: https://doi.org/10.1007/978-3-319-73888-8_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73887-1

  • Online ISBN: 978-3-319-73888-8

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