UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems
This addresses the user inconvenience in biometric authentication for smartwatch payments, though it is incremental as it builds on existing datasets and methods.
The paper tackles the burden of enrolment in behavioural biometric systems by generating synthetic user-specific gestures from a few real ones, reducing the required real gestures by over 40% without increasing error rates.
Behavioural biometric authentication systems entail an enrolment period that is burdensome for the user. In this work, we explore generating synthetic gestures from a few real user gestures with generative deep learning, with the application of training a simple (i.e. non-deep-learned) authentication model. Specifically, we show that utilising synthetic data alongside real data can reduce the number of real datapoints a user must provide to enrol into a biometric system. To validate our methods, we use the publicly available dataset of WatchAuth, a system proposed in 2022 for authenticating smartwatch payments using the physical gesture of reaching towards a payment terminal. We develop a regularised autoencoder model for generating synthetic user-specific wrist motion data representing these physical gestures, and demonstrate the diversity and fidelity of our synthetic gestures. We show that using synthetic gestures in training can improve classification ability for a real-world system. Through this technique we can reduce the number of gestures required to enrol a user into a WatchAuth-like system by more than 40% without negatively impacting its error rates.