QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
This solves the problem of ensuring data quality and privacy in gaze-based applications for researchers and developers, representing an incremental advancement by combining existing techniques in a novel way.
The paper tackles the challenge of verifying gaze data quality at scale while addressing privacy concerns, proposing QualitEye as the first method for this purpose, which achieves high verification performance on MPIIFaceGaze and GazeCapture datasets with small runtime overhead in privacy-preserving versions.
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.