User Authentication on Earable Devices via Bone-Conducted Occlusion Sounds

2024-07-01

IEEE Transactions on Dependable and Secure Computing

AI Summary

To address the vulnerability of conventional biometrics (e.g., fingerprint, face) to spoofing attacks and the persistent trade-off between security and usability in mobile device authentication, this paper proposes TeethPass⁺—the first seamless authentication scheme leveraging bone-conducted occlusion sounds from bilateral ear canals, captured via ear-worn devices. It introduces dental occlusion–induced bone-conducted acoustics as a novel physiological biometric. Key innovations include spectrum-variance–driven event detection, time-frequency noise suppression, decoupled modeling of four-dimensional physiological traits (dental/skeletal morphology, occlusion pose, and acoustic response), and triplet-network–based embedding learning. Evaluated on 53 subjects, TeethPass⁺ achieves 98.6% authentication accuracy and 99.7% spoofing resistance, while demonstrating strong environmental robustness and zero user awareness during authentication.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Bio-inspired Learning

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

With the rapid development of mobile devices and the fast increase of sensitive data, secure and convenient mobile authentication technologies are desired. Except for traditional passwords, many mobile devices have biometric-based authentication methods (e.g., fingerprint, voiceprint, and face recognition), but they are vulnerable to spoofing attacks. To solve this problem, we study new biometric features which are based on the dental occlusion and find that the bone-conducted sound of dental occlusion collected in binaural canals contains unique features of individual bones and teeth. Motivated by this, we propose a novel authentication system, TeethPass<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="li-ieq1-3335368.gif"/></alternatives></inline-formula>, which uses earbuds to collect occlusal sounds in binaural canals to achieve authentication. First, we design an event detection method based on spectrum variance to detect bone-conducted sounds. Then, we analyze the time-frequency domain of the sounds to filter out motion noises and extract unique features of users from four aspects: teeth structure, bone structure, occlusal location, and occlusal sound. Finally, we train a Triplet network to construct the user template, which is used to complete authentication. Through extensive experiments including 53 volunteers, the performance of TeethPass<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="li-ieq2-3335368.gif"/></alternatives></inline-formula> in different environments is verified. TeethPass<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="li-ieq3-3335368.gif"/></alternatives></inline-formula> achieves an accuracy of 98.6% and resists 99.7% of spoofing attacks.

Problem

• Develops secure earable authentication using bone-conducted occlusion sounds
• Extracts unique dental and bone features for user identification
• Resists spoofing attacks with high accuracy (98.6%)

Innovation

• Uses bone-conducted occlusion sounds for authentication
• Detects sounds via spectrum variance event detection
• Extracts unique features using Triplet network training