A Tap Is Your Key: Authentication by Tapping on the Face With a Wearable IMU

TY - JOUR

T1 - A Tap Is Your Key

T2 - Authentication by Tapping on the Face With a Wearable IMU

AU - Cao, Yetong

AU - Li, Fan

AU - Liu, Xiaochen

AU - Meng, Ling

AU - Wang, Yu

N1 - Publisher Copyright: © 2014 IEEE.

PY - 2026/3/15

Y1 - 2026/3/15

N2 - 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 biometric leakages. To address these limitations, this article 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 (ER) 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 reregistration capabilities upon traditionally invariant biometrics. This solution effectively eliminates concerns surrounding permanent biometrics leakage and enables accurate authentication in both single-user and multiuser 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.

AB - 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 biometric leakages. To address these limitations, this article 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 (ER) 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 reregistration capabilities upon traditionally invariant biometrics. This solution effectively eliminates concerns surrounding permanent biometrics leakage and enables accurate authentication in both single-user and multiuser 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.

KW - Inertial measurement unit (IMU)

KW - user authentication

KW - wrist-worn wearables

UR - https://www.scopus.com/pages/publications/105026669291

U2 - 10.1109/JIOT.2025.3649901

DO - 10.1109/JIOT.2025.3649901

M3 - Article

AN - SCOPUS:105026669291

SN - 2327-4662

VL - 13

SP - 11043

EP - 11054

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

IS - 6

ER -