文献类型：期刊文献

英文题名：Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

作者：Jiang, Xinyu[1];Xu, Ke[1];Liu, Xiangyu[2];Dai, Chenyun[1];Clifton, David A.[3];Clancy, Edward A.[4];Akay, Metin[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]Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford OX1 2JD, England;[4]Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA;[5]Univ Houston, Dept Biomed Engn, Houston, TX 77204 USA

年份：2021

卷号：25

期号：4

起止页码：1070

外文期刊名：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

收录：;EI(收录号：20211610217903);WOS:【SCI-EXPANDED(收录号:WOS:000638401400017)】；

基金：This work was supported in part by the National Key R&D 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：Task analysis; Biometrics (access control); Electrodes; Informatics; Muscles; Feature extraction; Signal to noise ratio; Biometrics; high-density sEMG; machine learning; cross-application discrepant identity recognition

摘要：With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometric tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.8% for 44 identities (22 subjects x 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications, with signal variation across days considered.