  Eye movement biometrics (EMB) is a relatively recent behavioral biometric
modality that may have the potential to become the primary authentication
method in virtual- and augmented-reality devices due to their emerging use of
eye-tracking sensors to enable foveated rendering techniques. However, existing
EMB models have yet to demonstrate levels of performance that would be
acceptable for real-world use. Deep learning approaches to EMB have largely
employed plain convolutional neural networks (CNNs), but there have been many
milestone improvements to convolutional architectures over the years including
residual networks (ResNets) and densely connected convolutional networks
(DenseNets). The present study employs a DenseNet architecture for end-to-end
EMB and compares the proposed model against the most relevant prior works. The
proposed technique not only outperforms the previous state of the art, but is
also the first to approach a level of authentication performance that would be
acceptable for real-world use.
