Work

Gaze Error Estimation and Linear Transformation to Improve Accuracy of Video-Based Eye Trackers

Year: 2025

Type: article

Abstract: Eye tracking technology plays a crucial role in various fields such as psychology, medical training, marketing, and human-computer interaction. However, achieving high accuracy over a larger field of view in eye tracking systems remains a significant challenge, both in free viewing and in a head-stabilized condition. In this paper, we propose a simple approach to improve the accuracy of video-based eye trackers through the implementation of linear coordinate transformations. This method involves applying stretching, shearing, translation, or their combinations to correct gaze accuracy errors. Our investigation shows that re-calibrating the eye tracker via linear transformations significantly improves the accuracy of video-based tracker over a large field of view.

Source: Vision

DOI: 10.3390/vision9020029

Authors: Varun Padikal, Alex Plonkowski, Penelope F. Lawton, Laura K. Young, Jenny C. A. Read

Institution: Newcastle University

Language: English

Topic: Gaze Tracking and Assistive Technology

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: Newcastle University