Exploring Earable-Based Passive User Authentication via Interpretable In-Ear Breathing Biometrics

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Mobile Computing Pub Date : 2024-09-03 DOI:10.1109/TMC.2024.3453412
Feiyu Han;Panlong Yang;Yuanhao Feng;Haohua Du;Xiang-Yang Li
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Abstract

As earable devices have become indispensable smart devices in people's lives, earable-based user authentication has gradually attracted widespread attention. In our work, we explore novel in-ear breathing biometrics and design an earable-based authentication approach, named BreathSign , which takes advantage of inward-facing microphones on commercial earphones to capture in-ear breathing sounds for passive authentication. To expand the differences among individuals, we model the process of breathing sound generation, transmission, and reception. Based on that, we derive hard-to-forge physical-level features from in-ear breathing sounds as biometrics. Furthermore, to eliminate the impact of breathing behavioral patterns (e.g., duration and intensity), we design a triple network model to extract breathing behavior-independent features and design an online user template update mechanism for long-term authentication. Extensive experiments with 35 healthy subjects have been conducted to evaluate the performance of BreathSign . The results show that our system achieves the average authentication accuracy of 93.15%, 98.06%, and 99.74% via one, five, and nine breathing cycles, respectively. Regarding the resistance of spoofing attacks, BreathSign could achieve an average EER of approximately 3.5%. Compared with other behavior-based authentication schemes, BreathSign does not require users to perform complex movements or postures but only effortless breathing for authentication and can be easily implemented on commercial earphones with high usability and enhanced security.
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通过可解释耳内呼吸生物识别技术探索基于耳朵的被动用户身份验证技术
随着可听设备成为人们生活中不可或缺的智能设备,基于可听设备的用户身份验证也逐渐受到广泛关注。在我们的工作中,我们探索了新型耳内呼吸生物识别技术,并设计了一种基于耳机的身份验证方法,命名为 BreathSign,该方法利用商用耳机上的内向麦克风捕捉耳内呼吸声,用于被动身份验证。为了扩大个体之间的差异,我们对呼吸声的产生、传播和接收过程进行了建模。在此基础上,我们从耳内式呼吸声中提取出难以伪造的物理层特征作为生物识别特征。此外,为了消除呼吸行为模式(如持续时间和强度)的影响,我们设计了一个三重网络模型来提取与呼吸行为无关的特征,并设计了一种在线用户模板更新机制,用于长期身份验证。我们对 35 名健康受试者进行了广泛的实验,以评估 BreathSign 的性能。结果表明,我们的系统通过一个、五个和九个呼吸周期分别实现了 93.15%、98.06% 和 99.74% 的平均认证准确率。在抵御欺骗攻击方面,BreathSign 的平均 EER 约为 3.5%。与其他基于行为的身份验证方案相比,BreathSign 不需要用户做复杂的动作或姿势,只需轻松呼吸即可进行身份验证,而且可以在商用耳机上轻松实现,具有很高的可用性和更强的安全性。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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