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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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In the world of digital communication, authentication is an important concern and the need for a safe and secure system increases the necessity of designing authentication systems. To perform authentication, biometric-based approaches are of great interest due to the property of being alive and resistant to forgery. In this study, an authentication system based on heart signal is designed. Due to the process of receiving heart signals, their data usually has a lot of noise. In order to prepare the data, in the proposed system, the heart signals are first cleaned and then transferred to the frequency domain for feature extraction. Also, they are converted into an image by applying the Wigner-Ville distribution, so that each image contains the signal information of each person’s heart and is unique. In the proposed authentication system, these images are used for training and evaluation in a deep convolutional neural network. The output of this system provides the possibility of people’s identification. The data of this study are taken from the NSRDB and MITDB databases, and significant results have been obtained compared to previous studies.