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.

翻译：行为生物特征认证系统通常需要一段令用户负担较重的注册期。本研究探索利用生成式深度学习从少量真实用户手势中生成合成手势，并应用于训练简单（即非深度学习）的认证模型。具体而言，我们证明将合成数据与真实数据结合使用，可以减少用户在生物特征系统中注册所需提供的真实数据点数量。为验证方法有效性，我们采用2022年提出的WatchAuth公开数据集——该系统通过用户伸手接近支付终端的物理手势实现智能手表支付认证。我们开发了正则化自编码器模型，用于生成代表这些物理手势的用户特定腕部运动合成数据，并展示了合成手势的多样性与保真度。实验表明，在训练中使用合成手势能够提升实际系统的分类性能。通过该技术，我们可将WatchAuth类系统注册所需手势数量减少40%以上，同时不影响其错误率。