Enabling Finger-Touch-Based Mobile User Authentication via Physical Vibrations on IoT Devices

• Published in: IEEE transactions on mobile computing Vol. 21; no. 10; pp. 3565 - 3580
• Main Authors: Yang, Xin, Yang, Song, Liu, Jian, Wang, Chen, Chen, Yingying, Saxena, Nitesh
• Format: Magazine Article
• Language: English
• Published: Los Alamitos IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Authentication; Biometrics; Feature extraction; finger-input; Layout; Low cost; Mobile computing; physical vibration; Physiology; Prototypes; Solid surfaces; Solids; Touch screens; Touch sensitive screens; ubiquitous surfaces; User authentication; Vibration; Vibrations
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2021.3057083

This work enables mobile user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. The system we proposed extends finger-input authentication beyond touch screens to any solid surface for IoT devices (e.g., smart access systems and IoT appliances). Unlike passcode or biometrics-based solutions, it integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. The proposed system builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. New algorithms are developed to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics including contacting area, touching force, and etc. The system is implemented using a single pair of low-cost portable vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a stovetop or an appliance). Extensive experiments demonstrate that our system can authenticate users with high accuracy (e.g., more than 97 percent within two trials), low false positive rate (e.g., less 2 percent) and is robust to various types of attacks.