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Gaze Authentication: Factors Influencing Authentication Performance

arXiv 2509.10969 ↗First posted 13 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

This paper examines the key factors that influence the performance of state-of-the-art gaze-based authentication. Experiments were conducted on a large-scale, in-house dataset comprising 8,849 subjects collected with Meta Quest Pro equivalent hardware running a video oculography-driven gaze estimation pipeline at 72~Hz. State of the neural network architecture was employed to study the influence of the following factors on authentication performance: eye tracking signal quality, various aspects of eye tracking calibration, and simple filtering on estimated raw gaze. This report provides performance results and their analysis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 62 references that are in the map. First posted on arXiv 13 Sep 2025.

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References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2019Multi-Similarity Loss with General Pair Weighting for Deep Metric Learningcited by 368
  4. 2019Deep Eyedentification: Biometric Identification using Micro-Movements of the Eyecited by 25
  5. 2020GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Datasetcited by 20
  6. 2021Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Datasetcited by 11
  7. 2022GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual realitycited by 11
  8. 2016A Score-level Fusion Method for Eye Movement Biometricscited by 10
  9. 2024EmMixformer: Mix transformer for eye movement recognitioncited by 9
  10. 2024Evaluation of Eye Tracking Signal Quality for Virtual Reality Applications: A Case Study in the Meta Quest Procited by 6
  11. 2020Eye Movements Biometrics: A Bibliometric Analysis from 2004 to 2019cited by 4
  12. 2024Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Datasetcited by 4
5 more
  1. 2024Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulationcited by 4
  2. 2022Demonstrating Eye Movement Biometrics in Virtual Realitycited by 3
  3. 2023Signal vs Noise in Eye-tracking Data: Biometric Implications and Identity Information Across Frequenciescited by 3
  4. 2024Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipelinecited by 2
  5. 2025Ocular Authentication: Fusion of Gaze and Periocular Modalitiescited by 2

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