QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
Mayar Elfares,
Pascal Reisert,
Ralf Küsters,
Andreas Bulling
Proc. ACM on Human-Computer Interaction (PACM HCI),
2026.
Abstract
BibTeX
Project
Gaze-based applications are increasingly advancing with the availability of large datasets but ensuring data quality presents a substantial challenge when collecting data at scale. It further requires different parties to collaborate, therefore, privacy concerns arise. We propose QualitEye - the first method for verifying image-based gaze data quality. QualitEye employs a new semantic representation of eye images that contains the information required for verification while excluding irrelevant information for better domain adaptation. QualitEye covers a public setting where parties can freely exchange data and a privacy-preserving setting where parties cannot reveal their raw data nor derive gaze features/labels of others with adapted private set intersection protocols. We evaluate QualitEye on the MPIIFaceGaze and GazeCapture datasets and achieve a high verification performance (with a small overhead in runtime for privacy-preserving versions). Hence, QualitEye paves the way for new gaze analysis methods at the intersection of machine learning, human-computer interaction, and cryptography.
@inproceedings{elfares26_etra,
title = {{QualitEye}: {Public} and {Privacy}-preserving {Gaze} {Data} {Quality} {Verification}},
shorttitle = {{QualitEye}},
author = {Elfares, Mayar and Reisert, Pascal and Küsters, Ralf and Bulling, Andreas},
year = {2026},
booktitle = {Proc. ACM on Human-Computer Interaction (PACM HCI)},
number = {ETRA}
}
