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Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning

NCJ Number
Sensors Volume: 20 Issue: 5 Dated: 2020
Date Published

This study proposed and validated 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 percent 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 enables continuous identification and verification. (publisher abstract modified)

Date Published: January 1, 2020