Poziv na IEEE predavanje: Non-Contact Radar-Based Continuous Identity Authentication in Multiple-Subject Environments

Abstract: An unobtrusive 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 user changes or subsequent access to personal information. The research to be presented is focused on creating a non-contact 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. A key advantage of this radar technique is that continuous authentication is achieved without intrusive video imaging. While prior results focused on the use of respiratory motion to identify a single isolated subject, the challenge of resolving and identifying multiple subjects within the radar field of view is addressed here.  This research introduces an SNR-based decision algorithm that coherently combines two separation methods to overcome multiple subject monitoring limits. The hybrid-approach system manages well-spaced subjects. those at the edge of the antenna beamwidth or beyond, using a Direction of Arrival (DOA) approach, and more closely spaced subjects by employing Independent Component Analysis with the JADE Algorithm (ICA-JADE) to isolate individual respiratory signatures. Additionally, highly distinguishable breathing dynamics related to hyper-features (inhale area, exhale area, and breathing depth) can be extracted from the radar-captured respiration patterns to facilitate individual subject identification. Extracted hyper feature sets for 20 subjects measured with a 24-GHz radar system were used to train and test two different popular machine learning classifiers (K-nearest neighbor (KNN) and Support vector machine (SVM)). SVM with quadratic kernel outperformed the other classifiers, with an accuracy of 97.5%.

Short Bio: Dr. Shekh Md Mahmudul Islam (Member, IEEE) received the B.Sc. (Hons.) and M.Sc. degrees in Electrical and Electronic Engineering from the University of Dhaka, Dhaka, Bangladesh, in 2012 and 2014, respectively. He also received a Ph.D. degree in Electrical Engineering from the University of Hawaii at Manoa, Hawaii, USA, with a focus on biomedical applications incorporating RF/Microwave technologies in December 2020. He is currently serving as an Assistant Professor in the Department of Electrical and Electronic Engineering of the University of Dhaka, Dhaka, Bangladesh. His research interests include radar systems, antenna array signal processing, adaptive filter technique, and machine learning classifiers for pattern recognition. In Summer 2019, he also worked as a Radar System and Applications Engineering Intern with ON Semiconductor, Phoenix, AZ, USA. He also worked as a radio-frequency propagation intern with former Google life sciences (Verily), Mountain View, CA, USA and Adnoviv, HI, USA in Summer 2020. Dr. Islam has also been serving as an affiliate member of the technical committee of the MTT-28 Biological Effects and Medical Applications of the IEEE Microwave Theory and Technique Society. He was a recipient of the 2020 University of Hawaii at Manoa Department of Electrical Engineering Research Excellence Award. He was also the Student Paper Finalist in IEEE Radio Wireless Week (RWW’19) Conference, which was held in FL, USA. He has also been serving as an Editorial Board Member of Frontiers in Physiology Journal and as a review editor of IEEE Access, IEEE Sensor, IEEE Transactions on Microwave Theory and Technique, MDPI Sensor, Frontiers in Sensors, and Frontiers in Communication and Network journals.