  The permanence of eye movements as a biometric modality remains largely
unexplored in the literature. The present study addresses this limitation by
evaluating a novel exponentially-dilated convolutional neural network for eye
movement authentication using a recently proposed longitudinal dataset known as
GazeBase. The network is trained using multi-similarity loss, which directly
enables the enrollment and authentication of out-of-sample users. In addition,
this study includes an exhaustive analysis of the effects of evaluating on
various tasks and downsampling from 1000 Hz to several lower sampling rates.
Our results reveal that reasonable authentication accuracy may be achieved even
during both a low-cognitive-load task and at low sampling rates. Moreover, we
find that eye movements are quite resilient against template aging after as
long as 3 years.
