# AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers

arXiv: https://arxiv.org/abs/2605.11901 · HTML: https://mlsanity.com/paper/2605.11901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 74 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Evaluation of PPG Biometrics for Authentication in different states](https://mlsanity.com/paper/1712.08583/) (2017)
- [Continuous Authentication for Voice Assistants](https://mlsanity.com/paper/1701.04507/) (2017)
- [In-ear EEG biometrics for feasible and readily collectable real-world person authentication](https://mlsanity.com/paper/1705.03742/) (2017)
- [OESense: Employing Occlusion Effect for In-ear Human Sensing](https://mlsanity.com/paper/2106.08607/) (2021)
- [EarGate: Gait-based User Identification with In-ear Microphones](https://mlsanity.com/paper/2108.12305/) (2021)
- [Enabling Cardiac Monitoring using In-ear Ballistocardiogram on COTS Wireless Earbuds](https://mlsanity.com/paper/2501.06744/) (2025)
- [Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios](https://mlsanity.com/paper/2503.23930/) (2025)
- [LubDubDecoder: Bringing Micro-Mechanical Cardiac Monitoring to Hearables](https://mlsanity.com/paper/2509.10764/) (2025)

## Abstract (arXiv)

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.