TY - GEN
T1 - A novel multimodal biometric authentication system using machine learning and blockchain
AU - Brown, Richard
AU - Bendiab, Gueltoum
AU - Shiaeles, Stavros
AU - Ghita, Bogdan
N1 - Funding Information:
This project has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement no. 786698. This work reflects authors? view and Agency is not responsible for any use that may be made of the information it contains.
PY - 2021/1/5
Y1 - 2021/1/5
N2 - 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.
AB - 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.
KW - Authentication
KW - Blockchain
KW - Machine learning
KW - Multimodal
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85101520696&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64758-2_3
DO - 10.1007/978-3-030-64758-2_3
M3 - Conference contribution
AN - SCOPUS:85101520696
SN - 9783030647575
T3 - Lecture Notes in Networks and Systems
SP - 31
EP - 46
BT - Selected Papers from the 12th International Networking Conference - INC 2020
A2 - Ghita, Bogdan
A2 - Shiaeles, Stavros
PB - Springer
T2 - 12th International Network Conference
Y2 - 21 September 2020 through 21 September 2020
ER -