Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics
This work addresses authentication challenges in virtual- and augmented-reality devices, representing an incremental improvement over existing deep learning approaches.
The paper tackled the problem of low performance in eye movement biometrics for authentication by proposing a DenseNet architecture, which outperformed prior state-of-the-art methods and approached acceptable real-world performance levels.
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.