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Related Experiment Video

Updated: Feb 5, 2026

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

Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations.

Anuradha Kar¹, Peter Corcoran²

¹Department of Electrical & Electronic Engineering, National University of Ireland, Galway H91 TK33, Ireland. a.kar2@nuigalway.ie.

Sensors (Basel, Switzerland)

|September 21, 2018

Summary

This study introduces standardized metrics and visualization tools for evaluating eye tracker performance. These methods aim to improve the reliability and usability of eye gaze data in research.

Keywords:

eye gaze estimation eye tracker graphical user interface metrics mobile devices open source performance evaluation standardization visualizations

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Last Updated: Feb 5, 2026

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

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Published on: November 14, 2018

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A Method to Quantify Visual Information Processing in Children Using Eye Tracking

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Published on: July 9, 2016

Area of Science:

Human-Computer Interaction
Computer Vision
Usability Engineering

Background:

Eye trackers are crucial for gaze data collection, but their accuracy and system behavior impact reliability.
Current research lacks standardized definitions for gaze estimation accuracy and robust methods for eye tracking system performance evaluation.

Purpose of the Study:

To develop and present fully defined evaluation metrics for comprehensive performance characterization of commercial eye trackers.
To implement visualization methods for assessing eye tracker performance and data quality across different platforms and designs.
To propose GazeVisual v1.1 software for integrating these metrics and visualizations, aiding general users in analyzing gaze datasets.

Main Methods:

Development of a novel set of precisely defined evaluation metrics for eye tracking systems.
Implementation of visualization techniques to analyze eye tracker performance and data quality.
Conceptualization of GazeVisual v1.1, a graphical user interface for integrated analysis.

Main Results:

A comprehensive set of evaluation metrics for eye tracker performance characterization.
Effective visualization methods for studying eye tracker data quality and performance.
A proposed software tool, GazeVisual v1.1, to streamline the analysis of gaze data.

Conclusions:

The developed metrics and visualizations offer a standardized approach to eye tracker evaluation.
GazeVisual v1.1 aims to empower users with accessible tools for effortless gaze data analysis.
These open resources will contribute to the standardization of gaze research outputs and analysis within the community.