A Tap is Your Key: Authentication by Tapping on the Face with a Wearable IMU

As wearables continue to gain widespread popularity, ensuring secure and convenient authentication becomes imperative to safeguard the data stored within these devices. However, existing wearable-based authentication solutions often rely on specialized and costly hardware, have limited applicability to specific scenarios, and are vulnerable to permanent biometrics leakages. To address these limitations, this paper explores the uniqueness of hand motions and subtle vibrations associated with face tapping. Specifically, we propose TapPass as a secure and convenient authentication solution that leverages face tapping signals captured via the Inertial Measurement Unit (IMU) in wrist-worn wearables for user authentication. To address significant interference from other body motions, we utilize the energy ratio and duration analysis of IMU measurements, followed by deep learning-based extraction of clean face tapping signals. Additionally, we explore the uniqueness of face tapping signals and extract effective features encompassing motion, vibration, and integral aspects. Based on these features, we generate cancelable biometrics leveraging a linear convolution-based approach, bestowing re-registration capabilities upon traditionally invariant biometrics. This solution effectively eliminates concerns surrounding permanent biometrics leakage and enables accurate authentication in both single-user and multi-user scenarios. Extensive experiments with 24 volunteers over three months demonstrate that TapPass achieves accurate authentication while effectively tackling major attacks and motion interference, all while maintaining user-friendliness.