Analysis of Embeddings Learned by End-to-End Machine Learning Eye
  Movement-driven Biometrics Pipeline
Mehedi Hasan Raju Lee Friedman Dillon J Lohr Oleg V Komogortsev
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.