Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning
KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering Division (CEMSE), King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/662099
MetadataShow full item record
AbstractIn 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.
CitationKhorshid, 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/s20051421
SponsorsThis work was supported in part by the U.S. National Institute of Justice under 2016-R2-CX-0014.
Except where otherwise noted, this item's license is described as This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.