In this paper, we show that the tap gesture, performed when a user 'taps' a smartwatch onto an NFC-enabled terminal to make a payment, is a biometric capable of implicitly authenticating the user and simultaneously recognising intent-to-pay. The proposed system can be deployed purely in software on the watch without requiring updates to payment terminals. It is agnostic to terminal type and position and the intent recognition portion does not require any training data from the user. To validate the system, we conduct a user study (n=16) to collect wrist motion data from users as they interact with payment terminals and to collect long-term data from a subset of them (n=9) as they perform daily activities. Based on this data, we identify optimum gesture parameters and develop authentication and intent recognition models, for which we achieve EERs of 0.08 and 0.04, respectively.
翻译:在本文中,我们显示,当用户“taps smartwatch” 在一个以NFC为支撑的终端上进行“智能观察”以支付款项时,执行的启动手势是一种生物鉴别方法,能够含蓄地认证用户,同时识别付款意向。拟议的系统可以完全在手表上的软件中部署,而无需更新付款终端。它对于终端类型和位置是不可知的,而意向识别部分并不要求用户提供任何培训数据。为了验证系统,我们进行了用户研究(n=16),从用户与付款终端互动时收集手腕运动数据,并从他们中的一组用户(n=9)进行日常活动时收集长期数据。根据这些数据,我们确定最佳的手势参数,并开发认证和意向识别模型,为此我们分别实现0.08和0.04的 EER。