Enhancing IoT Security via Cancelable HD-sEMG-Based Biometric Authentication Password, Encoded by Gesture
Enhancing IoT Security via Cancelable HD-sEMG-Based Biometric Authentication Password, Encoded by Gesture
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通过可取消的基于 HD-sEMG 的生物识别验证密码（由手势编码）增强物联网安全性
DOI：
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发表时间：
2021
影响因子：
10.6
通讯作者：
Wei Chen
中科院分区：
文献类型：
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作者：
Xinyu Jiang;Xiangyu Liu;Jiahao Fan;Xinming Ye;C. Dai;E. Clancy;D. Farina;Wei Chen
在基于无线体域网（WBAN）的物联网（IoT）应用中，通过可靠的用户认证来增强信息安全性已经引起越来越多的关注。传统生物识别的不可取消性（例如，一旦生物特征模板被暴露，用于用户认证的生物特征（例如，指纹）增加了隐私泄露风险，因为用户不能自愿地创建新模板。在这项工作中，我们提出了一个可取消的生物识别模式的基础上，高密度表面肌电图（HD-sEMG）编码的手势密码，用户身份验证。当用户执行规定的手势密码时，从前臂肌肉获取HD-sEMG信号（256个通道），形成他们的生物特征标记。研究了34种日常常用的替代手势。此外，为了减少物联网设备中的数据采集和传输负担，采用自动生成的密码特定信道掩码来减少活动信道的数量。HD-sEMG生物识别技术也很强大，采样率降低，进一步降低了功耗。当冒名顶替者输入错误的手势密码时，HD-sEMG生物识别技术实现了0.0013的低等误率（EER），并在20名受试者中进行了验证。即使冒名顶替者输入了正确的手势密码，HD-sEMG生物识别技术仍然达到了0.0273的EER。如果HD-sEMG生物特征模板被暴露，用户可以通过简单地将其更改为新的手势密码来取消它，EER为0.0013。据我们所知，这是第一项使用常见日常手势下的HD-sEMG信号作为生物特征标记的研究，并在不同的日子获得了训练和测试数据。
Enhancing information security via reliable user authentication in wireless body area network (WBAN)-based Internet-of-Things (IoT) applications has attracted increasing attention. The noncancelability of traditional biometrics (e.g., fingerprint) for user authentication increases the privacy disclosure risks once the biometric template is exposed, because users cannot volitionally create a new template. In this work, we propose a cancelable biometric modality based on high-density surface electromyogram (HD-sEMG) encoded by hand gesture password, for user authentication. HD-sEMG signals (256 channels) were acquired from the forearm muscles when users performed a prescribed gesture password, forming their biometric token. Thirty four alternative hand gestures in common daily use were studied. Moreover, to reduce the data acquisition and transmission burden in IoT devices, an automatically generated password-specific channel mask was employed to reduce the number of active channels. HD-sEMG biometrics were also robust with reduced sampling rate, further reducing power consumption. HD-sEMG biometrics achieved a low equal error rate (EER) of 0.0013 when impostors entered a wrong gesture password, as validated on 20 subjects. Even if impostors entered the correct gesture password, the HD-sEMG biometrics still achieved an EER of 0.0273. If the HD-sEMG biometric template was exposed, users could cancel it by simply changing it to a new gesture password, with an EER of 0.0013. To the best of our knowledge, this is the first study to employ HD-sEMG signals under common daily hand gestures as biometric tokens, with training and testing data acquired on different days.