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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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FreeGaze: A Framework for 3D Gaze Estimation Using Appearance Cues from a Facial Video.

Shang Tian1,2, Haiyan Tu1,2, Ling He3

  • 1College of Electrical Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|December 9, 2023
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Summary

This study introduces a novel framework for 3D gaze estimation from facial videos. The FG-Net model achieves high accuracy and efficiency, advancing the field of attention analysis through gaze tracking.

Keywords:
dual-branch CNNeye featuresface featuresgaze estimationimproved normalization

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Behavioral Neuroscience

Background:

  • Gaze behavior is a key indicator of human attention.
  • Estimating gaze from facial videos is challenging due to variations in appearance and head pose.
  • Accurate gaze estimation has applications in various fields, including user experience research and assistive technologies.

Purpose of the Study:

  • To develop a robust framework for 3D gaze estimation using appearance cues.
  • To improve the accuracy and computational efficiency of gaze estimation methods.
  • To introduce and evaluate a novel dual-branch convolutional neural network (FG-Net) for this task.

Main Methods:

  • An end-to-end facial landmark detection approach.
  • An improved normalization method using orthogonal matrices for enhanced accuracy and reduced computational time.
  • A dual-branch convolutional neural network (FG-Net) integrating eye and face features for 3D gaze vector estimation.

Main Results:

  • The improved normalization method demonstrated higher accuracy and lower computational time compared to existing methods.
  • FG-Net achieved remarkable accuracies of 3.11° on the MPIIGaze dataset and 2.75° on the EyeDiap dataset.
  • Ten-fold cross-validation confirmed the framework's effectiveness and state-of-the-art performance.

Conclusions:

  • The proposed framework offers a highly effective solution for 3D gaze estimation.
  • FG-Net represents a significant advancement in accurately estimating gaze direction from facial videos.
  • The study highlights the potential of appearance-based cues and deep learning for sophisticated gaze analysis.