QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
Mayar Elfares, Pascal Reisert, Ralf K\"usters, Andreas Bulling

TL;DR
QualitEye is a novel method for verifying the quality of image-based gaze data that balances accuracy with privacy preservation, enabling secure collaboration across different parties.
Contribution
It introduces a new semantic representation for gaze images and privacy-preserving protocols, facilitating data quality verification without revealing raw data.
Findings
High verification accuracy on MPIIFaceGaze and GazeCapture datasets.
Effective privacy-preserving verification with minimal runtime overhead.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGaze Tracking and Assistive Technology · Visual Attention and Saliency Detection · EEG and Brain-Computer Interfaces
MethodsSparse Evolutionary Training
