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Deep learning models for webcam eye tracking in online experiments.

Shreshth Saxena1,2, Lauren K Fink3,4,5, Elke B Lange3

  • 1Music Depart., Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany. shreshth.saxena@ae.mpg.de.

Behavior Research Methods
|August 23, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning eye tracking using webcams achieves high accuracy in remote psychological experiments. This advancement makes accessible and scalable eye tracking feasible for various research fields.

Keywords:
BlinksComputer visionDeep learningEye gazeEye trackingFixationFree viewingLow resolutionOnlineSmooth pursuit

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Area of Science:

  • Psychological Science
  • Cognitive Neuroscience
  • Human-Computer Interaction
  • User Experience Design

Background:

  • Eye tracking is crucial in scientific and commercial fields.
  • Deep learning and computer vision enable webcam-based eye tracking, reducing hardware costs.
  • Previous deep learning methods lack evaluation in remote, online psychological experiments.

Purpose of the Study:

  • To evaluate appearance-based deep learning methods for gaze and blink detection in remote, online eye tracking.
  • To address challenges specific to remote eye tracking setups.
  • To assess the performance of deep learning models across various eye movement tasks.

Main Methods:

  • 65 participants performed remote eye tracking tasks (fixation, zone classification, free viewing, smooth pursuit, blink detection) using their own laptops and webcams.
  • Webcam recordings were processed offline using appearance-based deep learning models for gaze and blink detection.
  • A comprehensive task battery characterized prediction accuracy across multiple measures.

Main Results:

  • Achieved a best gaze accuracy of 2.4° and precision of 0.47°.
  • Outperformed previous online eye tracking studies.
  • Significantly reduced the performance gap between laboratory-based and online eye tracking.

Conclusions:

  • Appearance-based deep learning methods are effective for remote, online eye tracking.
  • This approach offers affordable, accessible, and scalable eye tracking solutions.
  • The findings can accelerate research in psychological science, cognitive neuroscience, UX, and HCI.