# Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset

arXiv: https://arxiv.org/abs/2404.11798 · HTML: https://mlsanity.com/paper/2404.11798/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye](https://mlsanity.com/paper/1906.11889/) (2019)
- [GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset](https://mlsanity.com/paper/2009.06171/) (2020)
- [GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality](https://mlsanity.com/paper/2210.07533/) (2022)
- [Biometric Performance as a Function of Gallery Size](https://mlsanity.com/paper/1906.06272/) (2019)
- [Deep Distributional Sequence Embeddings Based on a Wasserstein Loss](https://mlsanity.com/paper/1912.01933/) (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)

## Built on by (most cited first, 4 of 4)

- [Ocular Authentication: Fusion of Gaze and Periocular Modalities](https://mlsanity.com/paper/2505.17343/) (2025)
- [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)
- [Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion](https://mlsanity.com/paper/2605.06810/) (2026)

## Abstract (arXiv)

This paper performs the crucial work of establishing a baseline for gaze-driven authentication performance to begin answering fundamental research questions using a very large dataset of gaze recordings from 9202 people with a level of eye tracking (ET) signal quality equivalent to modern consumer-facing virtual reality (VR) platforms. The size of the employed dataset is at least an order-of-magnitude larger than any other dataset from previous related work. Binocular estimates of the optical and visual axes of the eyes and a minimum duration for enrollment and verification are required for our model to achieve a false rejection rate (FRR) of below 3% at a false acceptance rate (FAR) of 1 in 50,000. In terms of identification accuracy which decreases with gallery size, we estimate that our model would fall below chance-level accuracy for gallery sizes of 148,000 or more. Our major findings indicate that gaze authentication can be as accurate as required by the FIDO standard when driven by a state-of-the-art machine learning architecture and a sufficiently large training dataset.