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Robust eye tracking based on multiple corneal reflections for clinical applications.

Clara Mestre1, Josselin Gautier1, Jaume Pujol1

  • 1Universitat Politècnica de Catalunya, Davalor Research Center (dRC), Terrassa, Spain.

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This study introduces advanced image processing and gaze estimation methods for precise eye tracking. Optimizing corneal reflections and pupil-glint vector normalization significantly enhances tracking accuracy, reducing the need for multiple light sources.

Keywords:
corneal reflectioneye trackinggaze estimationinterpolationpupil fittingpupil-glint vector

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Area of Science:

  • Computer Vision
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Accurate eye tracking is crucial for various applications, including virtual reality and assistive technologies.
  • Existing eye-tracking methods face challenges in achieving high resolution and accuracy, particularly with pupil and corneal reflection imaging.
  • The dark-pupil method offers high-resolution imaging but requires optimization for improved performance.

Purpose of the Study:

  • To propose novel image processing and gaze estimation methods for enhanced eye-tracking accuracy.
  • To analyze the impact of corneal reflection number and arrangement on eye-tracking performance.
  • To introduce a pupil-glint vector normalization technique to improve spatial accuracy.

Main Methods:

  • Utilized a dark-pupil eye-tracker with up to 12 corneal reflections for high-resolution imaging.
  • Investigated various light source configurations (12, 8, 6, 4, 2 corneal reflections).
  • Developed and applied a normalization factor for pupil-glint vectors in different configurations.

Main Results:

  • Eye-tracking accuracy generally increases with a higher number of corneal reflections, particularly in the vertical dimension (0.47° with 12 glints vs. 0.65° with 2 glints).
  • The arrangement of corneal reflections significantly impacts accuracy, with optimal configurations minimizing eyelid interference.
  • Pupil-glint vector normalization improved vertical eye-tracking accuracy by up to 43.2% and reduced the need for numerous light sources.

Conclusions:

  • The proposed methods, combining optimized corneal reflections and pupil-glint vector normalization, substantially improve eye-tracking accuracy.
  • Strategic arrangement of corneal reflections is as critical as their quantity for robust eye tracking.
  • Normalization techniques offer a more efficient path to high spatial accuracy in eye-tracking systems.