Name
Abstract | ||
|---|---|---|
User authentication and identification on smart devices has great significance in keeping data privacy and recommending personalized services. Existing few research works propose active sensing systems that emit and receive inaudible acoustic signals to authenticate users. But they share shortcomings of intrusiveness to users, high power consumption, and purely focusing on authentication. Instead, in this paper, we propose a passive sensing system called EarID with low-cost customized earpieces which attains user authentication and identification simultaneously. It makes use of a embedded microphone to sense body sounds spread out through ear canals and extract 'fingerprints' as a novel biometric feature. With self-designed earpieces, we design a deep learning-based real-time data processing pipeline. Extensive experiments under different real-world settings show that EarID can achieve a rather low false acceptance rate less than 5% for user authentication and a high F1 score of 96% for user identification.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3384419.3431254 | SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems
Virtual Event
Japan
November, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7590-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haibo Lei | 1 | 0 | 1.01 |
| Jinyuan Liu | 2 | 0 | 0.34 |
| Yongpan Zou | 3 | 118 | 9.06 |
| kaishun wu | 4 | 1059 | 94.59 |