  While numerous studies have explored eye movement biometrics since the
modality's inception in 2004, the permanence of eye movements remains largely
unexplored as most studies utilize datasets collected within a short time
frame. This paper presents a convolutional neural network for authenticating
users using their eye movements. The network is trained with an established
metric learning loss function, multi-similarity loss, which seeks to form a
well-clustered embedding space and directly enables the enrollment and
authentication of out-of-sample users. Performance measures are computed on
GazeBase, a task-diverse and publicly-available dataset collected over a
37-month period. This study includes an exhaustive analysis of the effects of
training 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 a low-cognitive-load task or at low sampling rates.
Moreover, we find that eye movements are quite resilient against template aging
after 3 years.
