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LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.

Sean Anthony Byrne¹, Virmarie Maquiling², Marcus Nyström³

¹MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy.

Behavior Research Methods

|March 31, 2025

Summary

This summary is machine-generated.

Light Eyes (LEyes) offers a novel framework for gaze estimation using simple synthetic data generation. This approach overcomes limitations of traditional methods, improving pupil and corneal reflection detection efficiency and accuracy.

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Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Deep learning advances gaze estimation but is limited by scarce, non-generalizable datasets.
Existing synthetic data methods are computationally intensive and require photorealistic rendering.
Hardware and biological diversity cause poor model generalization across datasets.

Purpose of the Study:

Introduce Light Eyes (LEyes), a novel framework for efficient gaze estimation.
Develop a method for on-the-fly synthetic data generation adaptable to various devices.
Improve the accuracy and efficiency of training neural networks for gaze estimation tasks.

Main Methods:

Utilized simple synthetic image generators instead of photorealistic ones.
Focused on generating key eye image features like pupils and corneal reflections.
Enabled on-the-fly data generation adaptable to any recording device.

Main Results:

LEyes outperforms existing methods in identifying and localizing pupils and corneal reflections.
Models trained with LEyes data show superior performance compared to standard eye trackers.
Achieved accurate gaze estimation with more cost-effective hardware.

Conclusions:

LEyes provides an efficient and adaptable solution for gaze estimation dataset generation.
The framework overcomes limitations of photorealistic synthetic data and scarce real-world data.
LEyes offers a promising direction for advancing gaze estimation technology with accessible hardware.