NON-CONTACT AND SECURE RADAR-BASED CONTINUOUS IDENTITY AUTHENTICATION IN MULTIPLE-SUBJECT ENVIRONMENTS

dc.contributor.advisor Lubecke , Victor M.
dc.contributor.author Islam, Shekh Md Mahmudul
dc.contributor.department Electrical Engineering
dc.date.accessioned 2021-02-08T21:15:22Z
dc.date.issued 2020
dc.description.degree Ph.D.
dc.embargo.liftdate 2023-02-08
dc.identifier.uri http://hdl.handle.net/10125/73307
dc.subject Electrical engineering
dc.subject Biomedical engineering
dc.subject Engineering
dc.subject Biomedical Doppler Radar
dc.subject Fuzzy Extractor
dc.subject Identity Authentication
dc.subject Machine Learning Classifiers
dc.subject Non-Contact Sensing
dc.title NON-CONTACT AND SECURE RADAR-BASED CONTINUOUS IDENTITY AUTHENTICATION IN MULTIPLE-SUBJECT ENVIRONMENTS
dc.type Thesis
dcterms.abstract An unobtrusive, secure and non-contact continuous authentication system can potentially improve security throughout a login session. Traditional user authentication procedures such as fingerprint, password, or facial identification typically provide only an initial spot-check of identity at the start of a user session, potentially allowing undesired access to personal information (e. g. social security number) at some later point in an apparently continuous user session. The research goal of this PhD dissertation is to create a non-contact and secure continuous authentication system based on Doppler radar, which analyzes back-scattered RF signals which carry body motion information indicating a human subject’s vital signs (breathing rate, heart rate) and associated unique patterns. An additional advantage to this radar technique is that continuous authentication is achieved without intrusive video imaging. Reported prior results are focused solely on use of respiratory motion to identify a single isolated subject. Simultaneous measurement of multiple subjects is a critical challenge. In realistic environments (airport security, in-home sleep apnea test, etc.) the presence of multiple subjects in front of the radar system is likely. To make this technology effective for real world applications, isolation of one particular subject’s breathing pattern from the combined mixture of motion for multiple subjects is essential. Reported research has so far been limited to maintaining 1-m subject separation based on the radar antenna beam-width. This thesis proposes a hybrid method consisting of an SNR-based intelligent decision algorithm which integrates two different approaches to isolate respiratory signatures of two-subjects within the radar antenna beamwidth separated by less than 1-m. A 24-GHz phase comparison Monopulse radar module (K-MC4) has been used to estimate the Direction of Arrival (DOA) for the physiological motion signals of well-spaced subjects at the edge of the beamwidth of the transceiver. DOA is inherently limited to the main beamwidth of the transceiver so when the subjects get closer, crossing the edge of the beamwidth, an additional independent component analysis with the JADE algorithm (ICA-JADE) process is employed to isolate individual respiratory signatures. Experimental results demonstrated that, this proposed SNR-based decision algorithm works with an accuracy of above 93%. In addition, angular location of each subject is estimated by phase-comparison monopulse and an integrated beam switching capability is also demonstrated to optimally extract respiratory information. Additionally, we also conducted a medium scale experiment with twenty participants and collected Doppler radar signals containing the combined respiration mixtures of every pair of participants, over the course of about one month. We then used our proposed SNR-based decision algorithm to separate respiratory signatures from the combined mixtures. From the separated respiratory signatures, we extracted highly distinguishable breathing dynamics-related hyper-features from the respiratory signatures including breathing rate, heart rate, inhale/exhale rate and inhale/exhale area for identity verification. We evaluated the hyper-feature sets with two different classifiers, k-nearest neighbor (KNN), and support vector machine (SVM), and achieved an accuracy 97.5%. We also analyzed the empirical entropy of the hyper-feature set and found that intrinsic entropy of the hyper-feature set is approximately 3-bits which is insufficient for secure identity verification. To improve the security of the proposed system, we also combined fuzzy extractors with linear coding to transform the breathing dynamic related feature into strong biometric keys compatible with machine learning classifiers. We also integrated this proposed radio-based identity verification system with in-home sleep apnea test scenarios. A compliance tracking switching protocol has been developed to integrate the radio-based identity verification system with OSA test. To the best of our knowledge this is the first attempt to achieve secure radio-based multi-subject identity verification by combining the Doppler radar and Fuzzy extractor.
dcterms.extent 243 pages
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.type Text
local.identifier.alturi http://dissertations.umi.com/hawii:10824
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