Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Science and Engineering Division (CEMSE), King Abdullah University of Science and Technology, Thuwal 23955, Saudi ArabiaComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2020-03-05Submitted Date
2020-02-01Permanent link to this record
http://hdl.handle.net/10754/662099
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In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.Citation
Khorshid, A. E., Alquaydheb, I. N., Kurdahi, F., Jover, R. P., & Eltawil, A. (2020). Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning. Sensors, 20(5), 1421. doi:10.3390/s20051421Sponsors
This work was supported in part by the U.S. National Institute of Justice under 2016-R2-CX-0014.Publisher
MDPI AGJournal
SensorsAdditional Links
https://www.mdpi.com/1424-8220/20/5/1421ae974a485f413a2113503eed53cd6c53
10.3390/s20051421
Scopus Count
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