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Improving eye-tracking calibration accuracy using symbolic regression.

Almoctar Hassoumi1,2, Vsevolod Peysakhovich2, Christophe Hurter1

  • 1DEVI, French Civil Aviation University - ENAC, Toulouse, France.

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|March 16, 2019
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Summary
This summary is machine-generated.

This study introduces symbolic regression for eye tracking calibration, improving accuracy by over 30%. This novel method enhances gaze position mapping without assuming polynomial functions, offering greater flexibility and precision in eye tracking systems.

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Human-Computer Interaction

Background:

  • Traditional eye tracking calibration relies on polynomial regressions.
  • Novel methods like smooth pursuit and vestibulo-ocular reflex calibrations collect more data.
  • Existing methods lack flexibility and accuracy in mapping gaze positions.

Purpose of the Study:

  • To introduce symbolic regression as a novel method for eye tracking calibration computation.
  • To compare symbolic regression-based calibrations with traditional methods.
  • To enhance the accuracy and flexibility of gaze position mapping.

Main Methods:

  • Developed a new calibration computation method using symbolic regression.
  • Designed two experiments to collect ground truth data.
  • Compared vestibulo-ocular reflex and smooth pursuit calibrations using marker and finger targets.

Main Results:

  • Symbolic regression improved calibration accuracy by over 30%.
  • Achieved higher accuracy compared to standard polynomial regression methods.
  • Demonstrated reasonable additional computation time for symbolic regression.

Conclusions:

  • Symbolic regression offers a flexible and accurate alternative for eye tracking calibration.
  • The proposed method significantly enhances gaze tracking performance.
  • This approach opens new perspectives for developing advanced eye tracking systems.