An artificial neural network framework for gait based biometrics

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Title: An artificial neural network framework for gait based biometrics
Author(s): Sun, Y
Lo, B
Item Type: Journal Article
Abstract: OAPA As the popularity of wearable and implantable Body Sensor Network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN devices are often not able to provide strong security mechanisms to protect sensitive personal and health information, such as one's physiological data. Consequently, many new methods of securing Wireless Body Area Networks (WBANs) have been proposed recently. One effective solution is the Biometric Cryptosystem (BCS) approach. BCS exploits physiological and behavioral biometric traits, including face, iris, fingerprints, Electrocardiogram (ECG), and Photoplethysmography (PPG). In this paper, we propose a new BCS approach for securing wireless communications for wearable and implantable healthcare devices using gait signal energy variations and an Artificial Neural Network (ANN) framework. By simultaneously extracting similar features from BSN sensors using our approach, binary keys can be generated on demand without user intervention. Through an extensive analysis on our BCS approach using a gait dataset, the results have shown that the binary keys generated using our approach have high entropy for all subjects. The keys can pass both NIST and Dieharder statistical tests with high efficiency. The experimental results also show the robustness of the proposed approach in terms of the similarity of intra-class keys and the discriminability of the inter-class keys.
Publication Date: 1-Aug-2018
Date of Acceptance: 21-Jul-2018
URI: http://hdl.handle.net/10044/1/64931
DOI: https://dx.doi.org/10.1109/JBHI.2018.2860780
ISSN: 2168-2194
Publisher: Institute of Electrical and Electronics Engineers
Journal / Book Title: IEEE Journal of Biomedical and Health Informatics
Copyright Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publication Status: Published online
Online Publication Date: 2018-08-02
Appears in Collections:Faculty of Engineering
Division of Surgery
Faculty of Medicine



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