Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics

The increasing usage of smartwatches to access sensitive and personal data while being applied in health monitoring and quick payment, has given rise to the need of convenient and secure authentication technique. However, traditional memory-based authentication methods like PIN are proved to be easily cracked or user-unfriendly. This paper presents a novel approach to unlock smartwatches or authenticate users’ identities on smartwatches by analyzing a users’ handwaving patterns. A filed study was conducted to design typical smartwatch unlocking scenarios and gather users’ handwaving data. Behavioral features were extracted to accurately characterize users’ handwaving patterns. Then a one-class classification algorithm based on scaled Manhattan distance was developed to perform the task of user authentication. Extensive experiments based on a newly established 150-person-time handwaving dataset with a smartwatch, are included to demonstrate the effectiveness of the proposed approach, which achieves an equal-error rate of 4.27% in free-shaking scenario and 14.46% in imitation-attack scenario. This level of accuracy shows that these is indeed identity information in handwaving behavior that can be used as a wearable authentication mechanism.