Deep learning models for webcam eye tracking in online experiments

Eye tracking is prevalent in scientific and commercial applications. Recent computer vision and deep learning methods enable eye tracking with off-the-shelf webcams and reduce dependence on expensive, restrictive hardware. However, such deep learning methods have not yet been applied and evaluated f...

Full description

Saved in:
Bibliographic Details
Published in:Behavior research methods 2024-04, Vol.56 (4), p.3487-3503
Main Authors: Saxena, Shreshth, Fink, Lauren K., Lange, Elke B.
Format: Article
Language:English
Subjects:
Adult
Behavioral Science and Psychology
Blinking - physiology
Cognitive Psychology
Computer vision
Deep Learning
Disclaimers
Eye movements
Eye Movements - physiology
Eye-Tracking Technology
Female
Fixation, Ocular - physiology
Humans
Male
Psychology
Smooth pursuit eye movements
Telematics
Webcams
Young Adult
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Eye tracking is prevalent in scientific and commercial applications. Recent computer vision and deep learning methods enable eye tracking with off-the-shelf webcams and reduce dependence on expensive, restrictive hardware. However, such deep learning methods have not yet been applied and evaluated for remote, online psychological experiments. In this study, we tackle critical challenges faced in remote eye tracking setups and systematically evaluate appearance-based deep learning methods of gaze tracking and blink detection. From their own homes and laptops, 65 participants performed a battery of eye tracking tasks including (i) fixation, (ii) zone classification, (iii) free viewing, (iv) smooth pursuit, and (v) blink detection. Webcam recordings of the participants performing these tasks were processed offline through appearance-based models of gaze and blink detection. The task battery required different eye movements that characterized gaze and blink prediction accuracy over a comprehensive list of measures. We find the best gaze accuracy to be 2.4° and precision of 0.47°, which outperforms previous online eye tracking studies and reduces the gap between laboratory-based and online eye tracking performance. We release the experiment template, recorded data, and analysis code with the motivation to escalate affordable, accessible, and scalable eye tracking that has the potential to accelerate research in the fields of psychological science, cognitive neuroscience, user experience design, and human–computer interfaces.
ISSN:1554-3528
1554-351X
1554-3528
DOI:10.3758/s13428-023-02190-6