文献类型：期刊文献

英文题名：Enhancing IoT Security via Cancelable HD-sEMG-Based Biometric Authentication Password, Encoded by Gesture

作者：Jiang, Xinyu[1];Liu, Xiangyu[2];Fan, Jiahao[1];Ye, Xinming[3];Dai, Chenyun[1];Clancy, Edward A.[4];Farina, Dario[5];Chen, Wei[1]

机构：[1]Fudan Univ, Ctr Intelligent Med Elect, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China;[2]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Sports Sci & Engn, Shanghai 200237, Peoples R China;[4]Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA;[5]Imperial Coll London, Dept Bioengn, London SW7 2AZ, England

年份：2021

卷号：8

期号：22

起止页码：16535

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20211710258391);WOS:【SCI-EXPANDED(收录号:WOS:000714714400035)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFE0112000; in part by Shanghai Pujiang Program under Grant 19PJ1401100; and in part by Shanghai Municipal Science and Technology Major Project under Grant 2017SHZDZX01.

语种：英文

外文关键词：Biometrics (access control); Wrist; Electrodes; Internet of Things; Task analysis; Authentication; Muscles; Biometrics; high-density surface electromyogram (HD-sEMG); Internet of Things (IoT); pattern recognition; user authentication

摘要：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.