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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.11901 |
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| _version_ | 1866917484706136064 |
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| author | Wang, Lei Shen, Jiangxuan Zhang, Xi Zhang, Dalin Li, Jingyu Dai, Haipeng Xu, Chenren Zhang, Daqing Huang, He |
| author_facet | Wang, Lei Shen, Jiangxuan Zhang, Xi Zhang, Dalin Li, Jingyu Dai, Haipeng Xu, Chenren Zhang, Daqing Huang, He |
| contents | 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 noise. To realize this, we first design a two-stage denoising scheme to suppress both inherent and sporadic interference. To extract user-specific features, we then propose a disentanglement-based deep learning model, HIDNet, which explicitly separates user-specific features from shared nuisance components. Lastly, we develop a scalable authentication framework based on a Siamese network that eliminates the need for per-user classifier training. We conduct extensive experiments with 33 participants, achieving an average FAR of 3.13% and FRR of 2.99%, which demonstrates the practical feasibility of AccLock. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11901 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers Wang, Lei Shen, Jiangxuan Zhang, Xi Zhang, Dalin Li, Jingyu Dai, Haipeng Xu, Chenren Zhang, Daqing Huang, He Cryptography and Security Artificial Intelligence 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 noise. To realize this, we first design a two-stage denoising scheme to suppress both inherent and sporadic interference. To extract user-specific features, we then propose a disentanglement-based deep learning model, HIDNet, which explicitly separates user-specific features from shared nuisance components. Lastly, we develop a scalable authentication framework based on a Siamese network that eliminates the need for per-user classifier training. We conduct extensive experiments with 33 participants, achieving an average FAR of 3.13% and FRR of 2.99%, which demonstrates the practical feasibility of AccLock. |
| title | AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2605.11901 |