# QualitEye: Public and Privacy-preserving Gaze Data Quality Verification

arXiv: https://arxiv.org/abs/2506.05908 · HTML: https://mlsanity.com/paper/2506.05908/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 108 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 30 of 30)

- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Self-supervised Pretraining of Visual Features in the Wild](https://mlsanity.com/paper/2103.01988/) (2021)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1711.09017/) (2017)
- [ClusterFit: Improving Generalization of Visual Representations](https://mlsanity.com/paper/1912.03330/) (2019)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [Unsupervised Representation Learning for Gaze Estimation](https://mlsanity.com/paper/1911.06939/) (2019)
- [A Differential Approach for Gaze Estimation](https://mlsanity.com/paper/1904.09459/) (2019)
- [Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash](https://mlsanity.com/paper/2111.06628/) (2021)
- [A survey of image labelling for computer vision applications](https://mlsanity.com/paper/2104.08885/) (2021)
- [Contrastive Representation Learning for Gaze Estimation](https://mlsanity.com/paper/2210.13404/) (2022)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Privacy-Aware Eye Tracking Using Differential Privacy](https://mlsanity.com/paper/1812.08000/) (2018)
- [Differential Privacy for Eye Tracking with Temporal Correlations](https://mlsanity.com/paper/2002.08972/) (2020)
- [Differential Privacy for Eye-Tracking Data](https://mlsanity.com/paper/1904.06809/) (2019)
- [Federated Learning for Appearance-based Gaze Estimation in the Wild](https://mlsanity.com/paper/2211.07330/) (2022)
- [Evaluating the Data Quality of Eye Tracking Signals from a Virtual Reality System: Case Study using SMI's Eye-Tracking HTC Vive](https://mlsanity.com/paper/1912.02083/) (2019)
- [Evaluation of Eye Tracking Signal Quality for Virtual Reality Applications: A Case Study in the Meta Quest Pro](https://mlsanity.com/paper/2403.07210/) (2024)
- [Gaze-enhanced Crossmodal Embeddings for Emotion Recognition](https://mlsanity.com/paper/2205.00129/) (2022)
- [Reporting Eye-Tracking Data Quality: Towards a New Standard](https://mlsanity.com/paper/2404.00620/) (2024)
- [Large-Scale MPC: Scaling Private Iris Code Uniqueness Checks to Millions of Users](https://mlsanity.com/paper/2405.04463/) (2024)
- [UN Handbook on Privacy-Preserving Computation Techniques](https://mlsanity.com/paper/2301.06167/) (2023)
- [PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation](https://mlsanity.com/paper/2402.18970/) (2024)
- [An Assessment of the Eye Tracking Signal Quality Captured in the HoloLens 2](https://mlsanity.com/paper/2111.07209/) (2021)
- [Investigation of Architectures and Receptive Fields for Appearance-based Gaze Estimation](https://mlsanity.com/paper/2308.09593/) (2023)

## Built on by (most cited first, 1 of 1)

- [Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR](https://mlsanity.com/paper/2609.04592/) (2026)

## Abstract (arXiv)

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.