Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform
IntroductionIdentity verification plays a crucial role in modern society, with applications spanning from online services to security systems. As the need for robust automatic authentication systems increases, various methodologies—software, hardware, and biometric—have been developed. Among these,...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2024.1463713/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850251803898150912 |
|---|---|
| author | Sajjad Maleki Lonbar Akram Beigi Nasour Bagheri Nasour Bagheri Pedro Peris-Lopez Carmen Camara |
| author_facet | Sajjad Maleki Lonbar Akram Beigi Nasour Bagheri Nasour Bagheri Pedro Peris-Lopez Carmen Camara |
| author_sort | Sajjad Maleki Lonbar |
| collection | DOAJ |
| description | IntroductionIdentity verification plays a crucial role in modern society, with applications spanning from online services to security systems. As the need for robust automatic authentication systems increases, various methodologies—software, hardware, and biometric—have been developed. Among these, biometric modalities have gained significant attention due to their high accuracy and resistance to falsification. This paper focuses on utilizing electrocardiogram (ECG) signals for identity verification, capitalizing on their unique, individualized characteristics.MethodsIn this study, we propose a novel identity verification framework based on ECG signals. Notable datasets, such as the NSRDB and MITDB, are employed to evaluate the performance of the system. These datasets, however, contain inherent noise, which necessitates preprocessing. The proposed framework involves two main steps: (1) signal cleansing to remove noise and (2) transforming the signals into the frequency domain for feature extraction. This is achieved by applying the Wigner-Ville distribution, which converts ECG signals into image data. Each image captures unique cardiac signal information of the individual, ensuring distinction in a noise-free environment. For recognition, deep learning techniques, particularly convolutional neural networks (CNNs), are applied. The GoogleNet architecture is selected for its effectiveness in processing complex image data, and is used for both training and testing the system.ResultsThe identity verification model achieved impressive results across two benchmark datasets. For the NSRDB dataset, the model achieved an accuracy of 99.3% and an Equal Error Rate (EER) of 0.8%. Similarly, for the MITDB dataset, the model demonstrated an accuracy of 99.004% and an EER of 0.8%. These results indicate that the proposed framework offers superior performance in comparison to alternative biometric authentication methods.DiscussionThe outcomes of this study highlight the effectiveness of using ECG signals for identity verification, particularly in terms of accuracy and robustness against noise. The proposed framework, leveraging the Wigner-Ville distribution and GoogleNet architecture, demonstrates the potential of deep learning techniques in biometric authentication. The results from the NSRDB and MITDB datasets reflect the high reliability of the model, with exceptionally low error rates. This approach could be extended to other biometric modalities or combined with additional layers of security to enhance its practical applications. Furthermore, future research could explore additional preprocessing techniques or alternative deep learning architectures to further improve the performance of ECG-based identity verification systems. |
| format | Article |
| id | doaj-art-532dcfc35ef8436da39bb697c3efec80 |
| institution | OA Journals |
| issn | 2673-253X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-532dcfc35ef8436da39bb697c3efec802025-08-20T01:57:49ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2024-12-01610.3389/fdgth.2024.14637131463713Deep learning based bio-metric authentication system using a high temporal/frequency resolution transformSajjad Maleki Lonbar0Akram Beigi1Nasour Bagheri2Nasour Bagheri3Pedro Peris-Lopez4Carmen Camara5CPS2 Lab, Department of Communication, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of IranDepartment of AI, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of IranCPS2 Lab, Department of Communication, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of IranSchool of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, IranComputer Science Department, Carlos III University of Madrid, Getafe, SpainComputer Science Department, Carlos III University of Madrid, Getafe, SpainIntroductionIdentity verification plays a crucial role in modern society, with applications spanning from online services to security systems. As the need for robust automatic authentication systems increases, various methodologies—software, hardware, and biometric—have been developed. Among these, biometric modalities have gained significant attention due to their high accuracy and resistance to falsification. This paper focuses on utilizing electrocardiogram (ECG) signals for identity verification, capitalizing on their unique, individualized characteristics.MethodsIn this study, we propose a novel identity verification framework based on ECG signals. Notable datasets, such as the NSRDB and MITDB, are employed to evaluate the performance of the system. These datasets, however, contain inherent noise, which necessitates preprocessing. The proposed framework involves two main steps: (1) signal cleansing to remove noise and (2) transforming the signals into the frequency domain for feature extraction. This is achieved by applying the Wigner-Ville distribution, which converts ECG signals into image data. Each image captures unique cardiac signal information of the individual, ensuring distinction in a noise-free environment. For recognition, deep learning techniques, particularly convolutional neural networks (CNNs), are applied. The GoogleNet architecture is selected for its effectiveness in processing complex image data, and is used for both training and testing the system.ResultsThe identity verification model achieved impressive results across two benchmark datasets. For the NSRDB dataset, the model achieved an accuracy of 99.3% and an Equal Error Rate (EER) of 0.8%. Similarly, for the MITDB dataset, the model demonstrated an accuracy of 99.004% and an EER of 0.8%. These results indicate that the proposed framework offers superior performance in comparison to alternative biometric authentication methods.DiscussionThe outcomes of this study highlight the effectiveness of using ECG signals for identity verification, particularly in terms of accuracy and robustness against noise. The proposed framework, leveraging the Wigner-Ville distribution and GoogleNet architecture, demonstrates the potential of deep learning techniques in biometric authentication. The results from the NSRDB and MITDB datasets reflect the high reliability of the model, with exceptionally low error rates. This approach could be extended to other biometric modalities or combined with additional layers of security to enhance its practical applications. Furthermore, future research could explore additional preprocessing techniques or alternative deep learning architectures to further improve the performance of ECG-based identity verification systems.https://www.frontiersin.org/articles/10.3389/fdgth.2024.1463713/fullidentity authenticationECG signalWigner-Ville distributionconvolutional neural networks (CNNs)GoogleNet architecturesignal preprocessing |
| spellingShingle | Sajjad Maleki Lonbar Akram Beigi Nasour Bagheri Nasour Bagheri Pedro Peris-Lopez Carmen Camara Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform Frontiers in Digital Health identity authentication ECG signal Wigner-Ville distribution convolutional neural networks (CNNs) GoogleNet architecture signal preprocessing |
| title | Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform |
| title_full | Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform |
| title_fullStr | Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform |
| title_full_unstemmed | Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform |
| title_short | Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform |
| title_sort | deep learning based bio metric authentication system using a high temporal frequency resolution transform |
| topic | identity authentication ECG signal Wigner-Ville distribution convolutional neural networks (CNNs) GoogleNet architecture signal preprocessing |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2024.1463713/full |
| work_keys_str_mv | AT sajjadmalekilonbar deeplearningbasedbiometricauthenticationsystemusingahightemporalfrequencyresolutiontransform AT akrambeigi deeplearningbasedbiometricauthenticationsystemusingahightemporalfrequencyresolutiontransform AT nasourbagheri deeplearningbasedbiometricauthenticationsystemusingahightemporalfrequencyresolutiontransform AT nasourbagheri deeplearningbasedbiometricauthenticationsystemusingahightemporalfrequencyresolutiontransform AT pedroperislopez deeplearningbasedbiometricauthenticationsystemusingahightemporalfrequencyresolutiontransform AT carmencamara deeplearningbasedbiometricauthenticationsystemusingahightemporalfrequencyresolutiontransform |