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.

验证眼动生物识别系统中通过机器学习获得的嵌入式方法是否与传统生物识别中所观察到的时间持久性相似，并对输入数据的长度和质量对眼动生物识别的性能和嵌入式的稳定性进行了实验和评估。

基于端到端机器学习眼动驱动的生物特征传输学习嵌入分析

Analysis of Embeddings Learned by End-to-End Machine Learning Eye  Movement-driven Biometrics Pipeline

Recent work on exploration in reinforcement learning (RL) has led to a series
of increasingly complex solutions to the problem. This increase in complexity
often comes at the expense of generality. Recent empirical studies suggest
that, when applied to a broader set of domains, some sophisticated exploration
methods are outperformed by simpler counterparts, such as {\epsilon}-greedy. In
this paper we propose an exploration algorithm that retains the simplicity of
{\epsilon}-greedy while reducing dithering. We build on a simple hypothesis:
the main limitation of {\epsilon}-greedy exploration is its lack of temporal
persistence, which limits its ability to escape local optima. We propose a
temporally extended form of {\epsilon}-greedy that simply repeats the sampled
action for a random duration. It turns out that, for many duration
distributions, this suffices to improve exploration on a large set of domains.
Interestingly, a class of distributions inspired by ecological models of animal
foraging behaviour yields particularly strong performance.

本文提出了一种基于时域的 ε- 贪心探索算法，通过重复随机采样的行为来提高探索效果，该算法在许多不同领域都有良好的表现。

时间延长下的 ε- 贪心探索

Temporally-Extended ε-Greedy Exploration

Although temporal persistence, or permanence, is a well understood
requirement for optimal biometric features, there is no general agreement on
how to assess temporal persistence. We suggest that the best way to assess
temporal persistence is to perform a test-retest study, and assess test-retest
reliability. For ratio-scale features that are normally distributed, this is
best done using the Intraclass Correlation Coefficient (ICC). For 10 distinct
data sets (8 eye-movement related, and 2 gait related), we calculated the
test-retest reliability ('Temporal persistence') of each feature, and compared
biometric performance of high-ICC features to lower ICC features, and to the
set of all features. We demonstrate that using a subset of only high-ICC
features produced superior Rank-1-Identification Rate (Rank-1-IR) performance
in 9 of 10 databases (p = 0.01, one-tailed). For Equal Error Rate (EER), using
a subset of only high-ICC features produced superior performance in 8 of 10
databases (p = 0.055, one-tailed). In general, then, prescreening potential
biometric features, and choosing only highly reliable features will yield
better performance than lower ICC features or than the set of all features
combined. We hypothesize that this would likely be the case for any biometric
modality where the features can be expressed as quantitative values on an
interval or ratio scale, assuming an adequate number of relatively independent
features.

本研究采用测试 - 重测评估方法，并使用 ICC 计算 10 个数据库中的生物特征的时序持久性，比较高 ICC 特征和低 ICC 特征以及所有特征集的生物特征表现，发现只选择高可靠性特征的生物特征表现优于所有特征集和低 ICC 特征，在时间保持、生物特征和基队模式匹配等领域有广阔的应用前景。