This paper expands on the foundational concept of temporal persistence in biometric systems, specifically focusing on the domain of eye movement biometrics facilitated by machine learning. Unlike previous studies that primarily focused on developing biometric authentication systems, our research delves into the embeddings learned by these systems, particularly examining their temporal persistence, reliability, and biometric efficacy in response to varying input data. Utilizing two publicly available eye-movement datasets, we employed the state-of-the-art Eye Know You Too machine learning pipeline for our analysis. We aim to validate whether the machine learning-derived embeddings in eye movement biometrics mirror the temporal persistence observed in traditional biometrics. Our methodology involved conducting extensive experiments to assess how different lengths and qualities of input data influence the performance of eye movement biometrics more specifically how it impacts the learned embeddings. We also explored the reliability and consistency of the embeddings under varying data conditions. Three key metrics (kendall's coefficient of concordance, intercorrelations, and equal error rate) were employed to quantitatively evaluate our findings. The results reveal while data length significantly impacts the stability of the learned embeddings, however, the intercorrelations among embeddings show minimal effect.

翻译：本文扩展了生物特征系统中时间持久性的基础概念，特别聚焦于机器学习驱动的眼动生物特征识别领域。不同于以往主要致力于开发生物特征认证系统的研究，本研究深入探讨这些系统所学习的嵌入，重点考察了它们在响应不同输入数据时的时间持久性、可靠性及生物特征效能。通过利用两个公开的眼动数据集，我们采用最先进的Eye Know You Too机器学习管道进行分析。我们旨在验证眼动生物特征中基于机器学习导出的嵌入是否继承了传统生物特征中观察到的时间持久性。我们的研究方法包括开展大量实验，以评估不同长度与质量的输入数据如何影响眼动生物特征的表现，更具体地说是如何影响所学习的嵌入。我们还探讨了嵌入在不同数据条件下的可靠性与一致性。采用三个关键指标（肯德尔和谐系数、互相关系数及等错误率）对研究结果进行定量评估。结果表明：尽管数据长度显著影响所学习嵌入的稳定性，但嵌入之间的互相关系数显示出极小的效应。