# GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset

arXiv: https://arxiv.org/abs/2009.06171 · HTML: https://mlsanity.com/paper/2009.06171/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 20 of 20)

- [Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset](https://mlsanity.com/paper/2104.10489/) (2021)
- [EmMixformer: Mix transformer for eye movement recognition](https://mlsanity.com/paper/2401.04956/) (2024)
- [PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus](https://mlsanity.com/paper/2403.00506/) (2024)
- [EMTeC: A Corpus of Eye Movements on Machine-Generated Texts](https://mlsanity.com/paper/2408.04289/) (2024)
- [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)
- [Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition](https://mlsanity.com/paper/2409.11652/) (2024)
- [Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics](https://mlsanity.com/paper/2201.02110/) (2022)
- [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)
- [Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models](https://mlsanity.com/paper/2304.13536/) (2023)
- [Recent Advancement in 3D Biometrics using Monocular Camera](https://mlsanity.com/paper/2401.02646/) (2024)
- [EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition](https://mlsanity.com/paper/2409.14432/) (2024)
- [Individual Topology Structure of Eye Movement Trajectories](https://mlsanity.com/paper/2205.10667/) (2022)
- [User Identification via Free Roaming Eye Tracking Data](https://mlsanity.com/paper/2403.09415/) (2024)
- [Privacy Enhancement for Gaze Data Using a Noise-Infused Autoencoder](https://mlsanity.com/paper/2508.10918/) (2025)
- [Gaze Authentication: Factors Influencing Authentication Performance](https://mlsanity.com/paper/2509.10969/) (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)
- [EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis](https://mlsanity.com/paper/2609.04501/) (2026)

## Abstract (arXiv)

This manuscript presents GazeBase, a large-scale longitudinal dataset containing 12,334 monocular eye-movement recordings captured from 322 college-aged subjects. Subjects completed a battery of seven tasks in two contiguous sessions during each round of recording, including a - 1) fixation task, 2) horizontal saccade task, 3) random oblique saccade task, 4) reading task, 5/6) free viewing of cinematic video task, and 7) gaze-driven gaming task. A total of nine rounds of recording were conducted over a 37 month period, with subjects in each subsequent round recruited exclusively from the prior round. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of subjects and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other emerging applications applying machine learning techniques to eye movement signal analysis.