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An overview of secure authentication methods using ECG biometrics with deep learning algorithms
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Narges Mokhtari   , Amirhossein Safari , Sadegh Sadeghi * |
| Department of Mathematics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran |
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Abstract: (1700 Views) |
| Biometric systems are an important technique for user identification in today's world, which have been welcomed due to their non-invasive nature and high resistance to forgery and fraud. Physiological and behavioral biomarkers are two main types of biometric identifiers. Behavioral identifiers, such as voice recognition, are based on human or even animal actions. Physiological biometrics, such as fingerprints and facial recognition, which have been used in our daily lives in the past years, are based on the physical characteristics of the human body. One of the various biometrics that have been investigated in studies in this field is the heart signal, which has been well used in authentication and identification systems due to its simple acquisition process compared to biomarkers such as the brain signal. In addition, there are valid databases on heart signal data, which the researchers of this issue refer to evaluate their systems. In this study, the analysis, analysis, and comparison of different authentication methods using heart signal biometrics have been studied. Also, in the following, the advantages and disadvantages of deep learning methods and models proposed in this field have been examined. In the final part, firstly, the implementation of the method presented in Fuster and Lopez's research is discussed, and then, to evaluate, we present the tests designed using the network created in this study, and after that, concluding based on the results. |
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| Keywords: Authentication, Electrocardiogram Signal, ECG, Deep Learning, Convolutional neural networks CNN, Signal Pre-Processing |
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Full-Text [PDF 2686 kb]
(1013 Downloads)
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Type of Study: Review Article |
Subject:
Special
Received: 2023/12/19 | Accepted: 2024/03/4 | Published: 2024/03/26
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