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Low-Cost Eye Tracking Calibration: A Knowledge-Based Study.

Gonzalo Garde1, Andoni Larumbe-Bergera1, Benoît Bossavit2

  • 1Department of Electrical, Electronic and Communications Engineering, Arrosadia Campus, Public University of Navarre, 31006 Pamplona, Spain.

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|August 10, 2021
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Summary

Calibration significantly enhances accuracy in low-resolution eye tracking systems. This study proposes a framework showing calibration improves deep learning-based gaze estimation by over 50%, crucial for off-the-shelf solutions.

Keywords:
calibrationgaze-estimationlow-resolutiontheoretical analysis

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Subject calibration is known to improve high-performance eye tracker accuracy.
  • The impact of calibration on low-resolution, off-the-shelf eye tracking systems remains under-explored.
  • Deep learning models are increasingly used for gaze estimation.

Purpose of the Study:

  • To develop a theoretical framework for measuring calibration effects in deep learning-based gaze estimation for low-resolution systems.
  • To quantify the accuracy improvements gained from calibration in real-world eye tracking scenarios.
  • To assess the potential of calibration to bridge the accuracy gap between low-resolution and high-resolution systems.

Main Methods:

  • Utilized the synthetic U2Eyes dataset with a fully connected network to isolate user-specific features (e.g., kappa angles).
  • Evaluated the impact of system calibration on real-world data using the I2Head dataset.
  • Developed a theoretical framework to measure calibration's effect on gaze estimation accuracy.

Main Results:

  • Demonstrated accuracy improvements exceeding 50% after calibration in low-resolution systems.
  • Showed that calibration is a critical factor for accurate gaze estimation, even in resource-constrained scenarios.
  • Theoretically achieved accuracy close to high-resolution systems (around 0.7°) with careful feature selection post-calibration.

Conclusions:

  • Calibration is essential for achieving high accuracy in low-resolution eye tracking systems.
  • Off-the-shelf eye trackers have significant potential for improvement through effective calibration strategies.
  • Further research into feature selection can optimize calibration for even better gaze estimation performance.