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dc.contributor.authorBrown, R
dc.contributor.authorBendiab, G
dc.contributor.authorShiaeles, S
dc.contributor.authorGhita, B
dc.date.accessioned2021-05-18T10:47:05Z
dc.date.available2021-05-18T10:47:05Z
dc.date.issued2021
dc.identifier.isbn9783030647575
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.urihttp://hdl.handle.net/10026.1/17125
dc.description.abstract

Secure user authentication has become an important issue in modern society as in many consumer applications, especially financial transactions, it is extremely important to prove the identity of the user. In this context, biometric authentication methods that rely on physical and behavioural characteristics have been proposed as an alternative for convolutional systems that rely on simple passwords, Personal Identification Number or tokens. However, in real-world applications, authentication systems that involve a single biometric faced many issues, especially lack accuracy and noisy data, which boost the research community to create multibiometric systems that involve a variety of biometrics. Those systems provide better performance and higher accuracy compared to other authentication methods. However, most of them are inconvenient and requires complex interactions from the user. Thus, in this paper, we present a multimodal authentication system that relies on machine learning and blockchain, intending to provide a more reliable, transparent, and convenient authentication mechanism. The proposed system combines tow important biometrics: fingerprint and face with age, and gender features. The supervised learning algorithm Decision Tree has been used to combine the results of the biometrics verification process and produce a confidence level related to the user. The initial experimental results show the efficiency and robustness of the proposed systems.

dc.format.extent31-46
dc.language.isoen
dc.publisherSpringer International Publishing
dc.titleA Novel Multimodal Biometric Authentication System Using Machine Learning and Blockchain
dc.typeconference
dc.typeConference Proceeding
plymouth.volume180
plymouth.publication-statusPublished
plymouth.journalLecture Notes in Networks and Systems
dc.identifier.doi10.1007/978-3-030-64758-2_3
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.identifier.eissn2367-3389
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1007/978-3-030-64758-2_3
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeConference Paper/Proceeding/Abstract


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