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.

翻译：基于注视的应用正随着大规模数据集的可用性而日益进步，但在数据收集过程中确保数据质量仍是一项重大挑战。这进一步要求不同参与方进行协作，因此引发了隐私问题。我们提出QualitEye——首个用于验证基于图像的注视数据质量的方法。QualitEye采用一种新的眼图语义表示方法，该表示包含验证所需信息，同时排除不相关信息以实现更好的领域自适应。QualitEye涵盖两种场景：公开场景中各方可自由交换数据，以及隐私保护场景中各方通过改进的私有集合交集协议既不能泄露原始数据，也不能推导他人的注视特征/标签。我们在MPIIFaceGaze和GazeCapture数据集上评估QualitEye，实现了高验证性能（隐私保护版本的运行时开销较小）。因此，QualitEye为机器学习、人机交互与密码学交叉领域的新型注视分析方法铺平了道路。