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Related Experiment Video

Updated: Sep 16, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Dual Focus-3D: A Hybrid Deep Learning Approach for Robust 3D Gaze Estimation.

Abderrahmen Bendimered¹, Rabah Iguernaissi¹, Mohamad Motasem Nawaf¹

¹Laboratoire d'Informatique et des Systèmes, CNRS UMR 7020, Aix-Marseille University, 13009 Marseille, France.

Sensors (Basel, Switzerland)

|July 12, 2025

Summary

This summary is machine-generated.

This study introduces Dual Focus-3D, a novel deep learning model for accurate gaze estimation by fusing eye images and 3D head pose. It achieves state-of-the-art results on the new EyeLis dataset.

Keywords:

3D gaze estimation EyeLis dataset computer vision multimodal fusion

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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Gaze estimation is crucial for understanding human attention.
Existing methods struggle in natural environments.
Applications range from assistive technology to virtual reality.

Purpose of the Study:

To develop a robust and accurate gaze estimation model.
To introduce a novel hybrid deep learning architecture.
To present a new dataset for training and evaluation.

Main Methods:

Developed Dual Focus-3D, a hybrid deep learning architecture.
Fused appearance-based eye features with 3D head orientation data.
Introduced the EyeLis dataset with 5206 annotated samples.

Main Results:

Achieved state-of-the-art performance with a Mean Absolute Error (MAE) of 1.64° on the EyeLis dataset.
Demonstrated effective generalization across synthetic and real datasets.
Showcased significant accuracy improvements by incorporating 3D spatial information.

Conclusions:

The Dual Focus-3D model offers enhanced accuracy and robustness in gaze estimation.
Multimodal feature fusion and 3D spatial information are key to improved performance.
The EyeLis dataset facilitates further research in 3D gaze prediction.