AirAuth: evaluating in-air hand gestures for authentication

Abstract

Secure authentication with devices or services that store sensitive and personal information is highly important. However, traditional password and pin-based authentication methods compromise between the level of security and user experience. AirAuth is a biometric authentication technique that uses in-air gesture input to authenticate users. We evaluated our technique on a predefined (simple) gesture set and our classifier achieved an average accuracy of 96.6% in an equal error rate (EER-)based study. We obtained an accuracy of 100% when exclusively using personal (complex) user gestures. In a further user study, we found that AirAuth is highly resilient to video-based shoulder surfing attacks, with a measured false acceptance rate of just 2.2%. Furthermore, a longitudinal study demonstrates AirAuth's repeatability and accuracy over time. AirAuth is relatively simple, robust and requires only a low amount of computational power and is hence deployable on embedded or mobile hardware. Unlike traditional authentication methods, our system's security is positively aligned with user-rated pleasure and excitement levels. In addition, AirAuth attained acceptability ratings in personal, office, and public spaces that are comparable to an existing stroke-based on-screen authentication technique. Based on the results presented in this paper, we believe that AirAuth shows great promise as a novel, secure, ubiquitous, and highly usable authentication method.