TY - JOUR
T1 - Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning
AU - Khorshid, Ahmed E.
AU - Alquaydheb, Ibrahim N.
AU - Kurdahi, Fadi
AU - Jover, Roger Piqueras
AU - Eltawil, Ahmed
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by the U.S. National Institute of Justice under 2016-R2-CX-0014.
PY - 2020/3/6
Y1 - 2020/3/6
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/662099
UR - https://www.mdpi.com/1424-8220/20/5/1421
UR - http://www.scopus.com/inward/record.url?scp=85081008523&partnerID=8YFLogxK
U2 - 10.3390/s20051421
DO - 10.3390/s20051421
M3 - Article
C2 - 32150911
SN - 1424-8220
VL - 20
SP - 1421
JO - Sensors
JF - Sensors
IS - 5
ER -