QualitEye: Public and Privacy-preserving Gaze Data Quality Verification

• URL: http://arxiv.org/abs/2506.05908v1
• Date: Fri, 06 Jun 2025 09:27:04 GMT
• Title: QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
• Authors: Mayar Elfares, Pascal Reisert, Ralf Küsters, Andreas Bulling,
• Abstract summary: QualitEye is the first method for verifying image-based gaze data quality.<n>It employs a new semantic representation of eye images that contains the information required for verification.<n>We evaluate QualitEye on the MPIIFaceGaze and GazeCapture datasets and achieve a high verification performance.
• Score: 13.5969071961191
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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