Eye Know You Too: Toward Viable End-to-End Eye Movement Biometrics for User Authentication

Abstract

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 (VR/AR) 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. The present study proposes an improved methodology for EMB with the goal of satisfying the FIDO Biometrics Requirements’ recommendation of 5% false rejection rate at 1-in-10,000 false acceptance rate. A DenseNet-based convolutional neural network is proposed that is memory-efficient, relatively quick to train, and has only ~123K learnable parameters. The model is trained over an array of different eye-tracking tasks to improve the generalizability of learned features. Authentication performance is evaluated on a held-out set of up to 59 individuals across different eye-tracking tasks, test-retest intervals, and with increasing amounts of data available for enrollment and authentication. The impact of degraded sampling rates and spatial precision on authentication performance is also briefly explored to set the stage for future research targeting modern VR/AR devices. 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.