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3D Gaze Estimation Using RGB-IR Cameras.

Moayad Mokatren1, Tsvi Kuflik1, Ilan Shimshoni1

  • 1The Department of Information Systems, University of Haifa, Mount Carmel, Haifa 3498838, Israel.

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Summary
This summary is machine-generated.

This study introduces a novel 3D gaze estimation framework for corneal imaging. It accurately tracks user attention using deep learning and a multi-camera headset, achieving high precision in real-world conditions.

Keywords:
3D gaze estimationcorneal imagingeye tracking

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Accurate 3D gaze estimation is crucial for understanding user attention in interactive systems.
  • Existing methods struggle with pupil detection in corneal images due to reflections.
  • Real-time, unobtrusive eye-tracking calibration remains a challenge.

Purpose of the Study:

  • To present a robust framework for 3D gaze estimation using corneal imaging.
  • To enable reliable pupil tracking and 3D eye model generation in real-time.
  • To develop an auto-calibration process that requires no user instruction.

Main Methods:

  • A headset with IR and RGB cameras for pupil tracking and corneal imaging.
  • Deep learning algorithms for real-time pupil detection and 3D eye model computation.
  • A novel approach to transform pupil positions between IR and RGB images for gaze detection.

Main Results:

  • Achieved a low 3D gaze error of 2.12 degrees.
  • Demonstrated high accuracy in corneal image acquisition with an IoU of 0.71.
  • Validated the framework's performance in realistic indoor and outdoor mobile scenarios.

Conclusions:

  • The proposed framework offers a reliable and accurate solution for 3D gaze estimation in corneal imaging.
  • The auto-calibration method is unobtrusive, simplifying user experience.
  • The system shows potential for diverse real-world mobile applications requiring precise gaze tracking.