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I2DNet - Design and Real-Time Evaluation of Appearance-based gaze estimation system.

L R D Murthy1, Siddhi Brahmbhatt2, Somnath Arjun1

  • 13D Lab, CPDM, Indian Institute of Science, Bangalore, India.

Journal of Eye Movement Research
|November 4, 2021
PubMed
Summary

This study introduces I2DNet, a novel deep learning model for accurate, subject-independent gaze estimation. Real-time interface evaluation showed significant improvements in selection time and accuracy compared to existing systems.

Keywords:
Convolutional Neural NetworksEye trackingUsability evaluationweb-cam based eye tracking

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Gaze estimation is crucial for human-computer interaction.
  • Appearance-based methods, leveraging deep learning, outperform traditional model-based approaches but struggle with cross-dataset generalization.
  • Real-time performance and subject independence remain challenges for current state-of-the-art gaze estimation systems.

Purpose of the Study:

  • To propose I2DNet, a novel deep learning architecture for improved subject-independent gaze estimation.
  • To evaluate I2DNet's performance in a real-time gaze-controlled interface.
  • To compare I2DNet against existing systems like Webgazer.js and OpenFace 2.0.

Main Methods:

  • Developed I2DNet, a novel deep learning architecture for gaze estimation.
  • Achieved state-of-the-art accuracy on MPIIGaze and RT-Gene datasets (4.3 and 8.4 degree MAE).
  • Implemented I2DNet as a gaze-controlled interface for a 9-block pointing and selection task, comparing it with Webgazer.js and OpenFace 2.0 in a user study with 16 participants.

Main Results:

  • I2DNet achieved state-of-the-art mean angular errors of 4.3° on MPIIGaze and 8.4° on RT-Gene.
  • In real-time evaluation, I2DNet significantly reduced selection time compared to Webgazer.js and OpenFace 2.0.
  • The proposed system also led to a statistically significant reduction in missed selections.

Conclusions:

  • I2DNet demonstrates superior subject-independent gaze estimation accuracy.
  • The system offers a robust and efficient gaze-controlled interface for real-time applications.
  • I2DNet represents a significant advancement in gaze estimation technology for interactive settings.