HandID: Towards Unobtrusive Gesture-independent User Authentication on Smartphones Using Vibration-based Hand Biometrics

Biometric authentication is pivotal in protecting user privacy and ensuring smartphone security. Recent research indicates that the vibration response of hands can serve as a biometric for user authentication on mobile devices. However, the various grip gestures of users can introduce significant noise, disrupting hand biometrics-related signals and compromising authentication performance. This paper introduces HandID, an unobtrusive and gesture-independent user authentication method for smartphones that does not require large amounts of data from different gestures. Unlike existing methods, HandID does not require users to maintain specific gestures or interact with the touchscreen. It utilizes the built-in vibration motor to generate active vibrations and sense the user's hand through responses captured by the onboard accelerometer. HandID employs an adversarial learning model to handle gesture variations and proposes a novel hand biometric generation model to reduce the enrollment data required from users. Comprehensive experiments with 50 subjects show that HandID achieves an authentication accuracy of 92.5%, with a false acceptance rate (FAR) of 5.2% and a false rejection rate (FRR) of 5.6%. Security analyses demonstrate that HandID is resistant to zero-effort, s tatistical, a nd h ill-climbing a ttacks, a nd a u sability study indicates high user acceptance of HandID.