SlowMo-enhancing mobile gesture-based authentication schemes via sampling rate optimization
In the era of network service, the user authentication becomes more indispensable but also vulnerable. Traditional user verification approaches such as PIN or pattern lock suffer from easy hacking and replica, motivating the research on many new approaches like gesture-based security. Compare to traditional authentications, the gesture-based security utilizes the user interacts with the device as a dynamic authentication pattern in real-time, offering higher complexity and better reliability. However, gesture-based security still lacks sufficient research on data sampling and preprocessing techniques on classification accuracy. In this work, we develop SlowMo, a novel gesture security technique for user classification in low sampling-rate environments. The proposed algorithm provides maximum classification accuracy at a sampling rate of 4Hz with extreme low power consumption suggesting a more capable adaptation to the security environment. It can achieve classification accuracy as high as 89% with power consumption negligible to the user.
