# GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality

arXiv: https://arxiv.org/abs/2210.07533 · HTML: https://mlsanity.com/paper/2210.07533/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2022.

## Built on by (most cited first, 11 of 11)

- [Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset](https://mlsanity.com/paper/2404.11798/) (2024)
- [Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipeline](https://mlsanity.com/paper/2402.16399/) (2024)
- [Ocular Authentication: Fusion of Gaze and Periocular Modalities](https://mlsanity.com/paper/2505.17343/) (2025)
- [Biometrics in Extended Reality: A Review](https://mlsanity.com/paper/2411.10489/) (2024)
- [Will You Be Aware? Eye Tracking-Based Modeling of Situational Awareness in Augmented Reality](https://mlsanity.com/paper/2508.05025/) (2025)
- [Gaze Authentication: Factors Influencing Authentication Performance](https://mlsanity.com/paper/2509.10969/) (2025)
- [Deep Learning-Based Visual Fatigue Detection Using Eye Gaze Patterns in VR](https://mlsanity.com/paper/2510.12994/) (2025)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)
- [Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion](https://mlsanity.com/paper/2605.06810/) (2026)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)
- [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)

We present GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,020 binocular recordings from a diverse population of 407 college-aged participants. Participants were recorded up to six times each over a 26-month period, each time performing a series of five different ET tasks: (1) a vergence task, (2) a horizontal smooth pursuit task, (3) a video-viewing task, (4) a self-paced reading task, and (5) a random oblique saccade task. Many of these participants have also been recorded for two previously published datasets with different ET devices, and some participants were recorded before and after COVID-19 infection and recovery. GazeBaseVR is suitable for a wide range of research on ET data in VR devices, especially eye movement biometrics due to its large population and longitudinal nature. In addition to ET data, additional participant details are provided to enable further research on topics such as fairness.