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Related Experiment Video

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High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods.

Fabricio Batista Narcizo1,2, Fernando Eustáquio Dantas Dos Santos3, Dan Witzner Hansen1

  • 1Eye Information Laboratory, Department of Computer Science, IT University of Copenhagen (ITU), 2300 Copenhagen, Denmark.

Vision (Basel, Switzerland)
|September 26, 2021
PubMed
Summary

This study improves eye-tracking accuracy by virtually aligning the eye-camera to the eye's optical axis. Geometric transformations enhance feature distribution, increasing precise gaze estimations in uncalibrated systems.

Keywords:
eye trackereye-trackinggaze-mapping calibrationhigh-accuracy gaze estimationuncalibrated setup

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Gaze estimation accuracy is crucial for eye-tracking systems.
  • Commercial eye trackers face challenges with uncalibrated setups and variable eye-camera locations.
  • Non-coplanarity of the eye plane and off-axis camera positions distort eye feature distribution.

Purpose of the Study:

  • To investigate the impact of eye-camera location on interpolation-based eye-tracking accuracy.
  • To propose novel geometric transformation methods for improving gaze estimation.
  • To enhance the precision of eye-tracking in uncalibrated and off-the-shelf devices.

Main Methods:

  • Experiments were conducted using simulated data and real-world data from 83 participants.
  • Proposed geometric transformations virtually align the eye-camera to the center of the eye's optical axis.
  • Gaussian analysis was used to evaluate gaze estimation accuracy within a -0.5° to 0.5° range.

Main Results:

  • Eye-camera location significantly deforms eye feature distribution when the camera is far from the optical axis.
  • The proposed geometric transformation methods effectively reshape eye feature distribution.
  • Compared to traditional methods, the proposed approach increases the number of high-accuracy gaze estimations.

Conclusions:

  • Virtual alignment of the eye-camera improves the accuracy and precision of interpolation-based eye-tracking.
  • The proposed geometric methods offer a viable solution for enhancing gaze estimation in uncalibrated eye-tracking systems.
  • This research contributes to the development of more reliable and accurate commercial eye-tracking devices.