AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers
Lei Wang (Soochow University, China), Jiangxuan Shen (Soochow University, China), Xi Zhang (Macquarie University, Australia), Dalin Zhang (Aalborg University, Denmark), Jingyu Li (Peking University, China), Haipeng Dai (Nanjing University, China), Chenren Xu (Peking University

TL;DR
AccLock is a passive, unobtrusive earphone-based authentication system that uses in-ear heartbeat signals and advanced deep learning techniques to verify users securely and reliably without active user involvement.
Contribution
The paper introduces a novel passive authentication system leveraging in-ear heartbeat signals, with a new denoising scheme and a disentanglement-based deep learning model for scalable user verification.
Findings
Achieved an average FAR of 3.13% and FRR of 2.99% in experiments with 33 participants.
Developed a two-stage denoising scheme to improve signal quality in in-ear BCG signals.
Implemented a Siamese network-based framework that scales without per-user classifier training.
Abstract
The widespread use of earphones has enabled various sensing applications, including activity recognition, health monitoring, and context-aware computing. Among these, earphone-based user authentication has become a key technique by leveraging unique biometric features. However, existing earphone-based authentication systems face key limitations: they either require explicit user interaction or active speaker output, or suffer from poor accessibility and vulnerability to environmental noise, which hinders large-scale deployment. In this paper, we propose a passive authentication system, called AccLock, which leverages distinctive features extracted from in-ear BCG signals to enable secure and unobtrusive user verification. Our system offers several advantages over previous systems, including zero-involvement for both the device and the user, ubiquitous, and resilient to environmental…
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