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The permanence of eye movements as a biometric modality remains largelyunexplored in the literature. The present study addresses this limitation byevaluating a novel exponentially-dilated convolutional neural network for eyemovement authentication using a recently proposed longitudinal dataset known asGazeBase. The network is trained using multi-similarity loss, which directlyenables the enrollment and authentication of out-of-sample users. In addition,this study includes an exhaustive analysis of the effects of evaluating onvarious tasks and downsampling from 1000 Hz to several lower sampling rates.Our results reveal that reasonable authentication accuracy may be achieved evenduring both a low-cognitive-load task and at low sampling rates. Moreover, wefind that eye movements are quite resilient against template aging after aslong as 3 years.

Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset