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Updated: Sep 6, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

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Rethinking Model-Based Gaze Estimation.

Harsimran Kaur¹, Swati Jindal¹, Roberto Manduchi¹

¹University of California, Santa Cruz.

Proceedings of the ACM on Computer Graphics and Interactive Techniques

|June 27, 2022

Summary

This summary is machine-generated.

This study introduces a novel geometric layer for end-to-end gaze estimation networks. The new method improves accuracy across different datasets and can predict gaze angles beyond training data ranges.

Keywords:

Eye Geometry Gaze Estimation Neural Networks

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Area of Science:

Computer Vision
Human-Computer Interaction
Machine Learning

Background:

Data-driven gaze tracking algorithms using CNNs show high accuracy.
End-to-end methods lack explainability and suffer from domain gap issues.
Existing methods struggle with cross-dataset generalization.

Purpose of the Study:

To propose a novel method for end-to-end gaze estimation.
To improve cross-dataset generalization and explainability in gaze tracking.
To embed eye geometry information into deep learning networks.

Main Methods:

Developed a novel 'geometric layer' to integrate eye geometry into CNNs.
Proposed an end-to-end gaze estimation network incorporating the geometric layer.
Conducted experiments using cross-dataset evaluation.

Main Results:

The proposed system outperforms state-of-the-art methods in cross-dataset evaluations.
Achieved competitive performance on within-dataset tests.
Demonstrated the ability to extrapolate gaze angles outside the training data range.

Conclusions:

Embedding eye geometry improves the robustness and generalizability of gaze estimation.
The novel method addresses the domain gap problem in gaze tracking.
The system offers improved accuracy and extrapolation capabilities for gaze estimation.