BAED: A secured biometric authentication system using ECG signal based on deep learning techniques

• Autorzy: Jaya Prakash Allam , Patro Kiran Kumar , Hammad Mohamed , Tadeusiewicz Ryszard , Pławiak Paweł

Identyfikatory

DOI

10.1016/j.bbe.2022.08.004

• Języki publikacji: EN

Abstrakty

EN

Biometric authentication technology has become increasingly common in our daily lives as information protection and control regulation requirements have grown worldwide. A biometric system must be simple, flexible, efficient, and secure from unauthorized access. The most suitable and flexible biometric traits are the face, fingerprint, palm print, voice, electrocardiogram (ECG), and iris. ECGs are difficult to falsify among these biometric traits and are less attack-prone. However, designing biometric systems based on ECG is very challenging. The major limitations of the existing techniques are that they require a large amount of training data and that they are trained and tested on an on-person database. To cope with these issues, this work proposes a novel biometric authentication scheme based on ECG detection called BAED. The system was developed based on deep learning algorithms, including a convolutional neural network (CNN) and a long-term memory (LSTM) network with a customized activation function. The authors evaluated the proposed model with on-and off-person databases including ECG-ID, Physikalisch-Technische Bundesanstalt (PTB), Check Your Bio-signals Here Initiative (CYBHi), and the University of Toronto Database (UofTDB). In addition to the standard performance parameters, certain key supportive identification parameters such as FMR, FNMR, FAR, and FRR were computed and compared to increase the model’s credibility.The proposed BAED system outperforms prior state-of-the-art approaches.

Słowa kluczowe

EN

biometrics system; convolutional neural network; CNN; CYBHi; electrocardiogram; ECG-ID; PTB; UofTDB

PL

układ biometryczny; sieć neuronowa konwolucyjna; CNN; CYBHi; elektrokardiogram; ECG-ID; PTB; UofTDB

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 4
• Strony: 1081--1093
• Opis fizyczny: Bibliogr. 75 poz., rys., tab., wykr.

Twórcy

autor

Jaya Prakash Allam

• Department of EC, National Institute of Technology Rourkela, Odisha-769008, India

autor

Patro Kiran Kumar

• Department of ECE, Aditya Institute of Technology and Management, Tekkali, India

autor

Hammad Mohamed

• Information Technology Dept., Faculty of Computers and Information, Menoufia University, Menoufia, Egypt

autor

Tadeusiewicz Ryszard

• AGH University of Science and Technology, Department of Biocybernetics and Biomedical Engineering, Krakow, Poland

autor

Pławiak Paweł

• Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland
• Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)