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Measurement and Analysis of Human Body Channel Response for Biometric Recognition
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- Authors
- Taewook Kang, Kwang-Il Oh, Jae-Jin Lee, Beom-Su Park, Wangrok Oh, Seong-Eun Kim
- Issue Date
- 2021-08
- Citation
- IEEE Transactions on Instrumentation and Measurement, v.70, pp.1-12
- ISSN
- 0018-9456
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- This article presents a highly secure and convenient biometric system for user recognition based on body channel characteristics for electric signal transmission. In the proposed framework, the user can provide reliable biometric features of body channel responses (BCRs) simply by touching an electrode surface on the device with a finger. To realize and verify the proposed approach, we acquired the BCR data from 15 subjects for approximately six weeks through experiments conducted in a customized measurement setup suited to the principle of signal transmission in the human body channel. The proposed BCR-based biometric feature (BBF) comprises a series of envelope vectors of the received BCR when applying up- and down-chirp signals to the human body, which is extracted by an interpolative method based on peak detection. The BBFs are effectively separable according to the subjects because the features magnify quantitative differences in the aspect of path losses and power delay profiles of individual BCRs for the frequency range between 1 and MHz. The classification performance was evaluated by splitting the dataset into {80\%} and 20% for the training and testing, respectively, using conventional machine learning algorithms with uncorrelated 40 session datasets for the respective subjects. The highest average classification accuracy was achieved by the kernel-based support vector machine approximately 95.8% without observable and biased misidentification cases among the subjects. In addition, the analysis of receiver operating characteristic curves shows that the proposed classifiers are robust to decision boundaries at various threshold settings.
- KSP Keywords
- Channel Characteristics, Chirp signals, Classification Performance, Electric signal, Electrode surface, Frequency range, Human Body, Machine Learning Algorithms, Measurement and analysis, Measurement setup, Path loss