Multimodal Approach for Enhancing Biometric Authentication
Unimodal biometric systems rely on a single source or unique individual biological trait for measurement and examination. Fingerprint-based biometric systems are the most common, but they are vulnerable to presentation attacks or spoofing when a fake fingerprint is presented to the sensor. To addres...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/9/168 |
| _version_ | 1797579351563173888 |
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| author | Nassim Ammour Yakoub Bazi Naif Alajlan |
| author_facet | Nassim Ammour Yakoub Bazi Naif Alajlan |
| author_sort | Nassim Ammour |
| collection | DOAJ |
| description | Unimodal biometric systems rely on a single source or unique individual biological trait for measurement and examination. Fingerprint-based biometric systems are the most common, but they are vulnerable to presentation attacks or spoofing when a fake fingerprint is presented to the sensor. To address this issue, we propose an enhanced biometric system based on a multimodal approach using two types of biological traits. We propose to combine fingerprint and Electrocardiogram (ECG) signals to mitigate spoofing attacks. Specifically, we design a multimodal deep learning architecture that accepts fingerprints and ECG as inputs and fuses the feature vectors using stacking and channel-wise approaches. The feature extraction backbone of the architecture is based on data-efficient transformers. The experimental results demonstrate the promising capabilities of the proposed approach in enhancing the robustness of the system to presentation attacks. |
| first_indexed | 2024-03-10T22:35:57Z |
| format | Article |
| id | doaj.art-1e240dd55e9e45818e6210fe8b208334 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T22:35:57Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-1e240dd55e9e45818e6210fe8b2083342023-11-19T11:24:31ZengMDPI AGJournal of Imaging2313-433X2023-08-019916810.3390/jimaging9090168Multimodal Approach for Enhancing Biometric AuthenticationNassim Ammour0Yakoub Bazi1Naif Alajlan2Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaUnimodal biometric systems rely on a single source or unique individual biological trait for measurement and examination. Fingerprint-based biometric systems are the most common, but they are vulnerable to presentation attacks or spoofing when a fake fingerprint is presented to the sensor. To address this issue, we propose an enhanced biometric system based on a multimodal approach using two types of biological traits. We propose to combine fingerprint and Electrocardiogram (ECG) signals to mitigate spoofing attacks. Specifically, we design a multimodal deep learning architecture that accepts fingerprints and ECG as inputs and fuses the feature vectors using stacking and channel-wise approaches. The feature extraction backbone of the architecture is based on data-efficient transformers. The experimental results demonstrate the promising capabilities of the proposed approach in enhancing the robustness of the system to presentation attacks.https://www.mdpi.com/2313-433X/9/9/168fingerprintmultimodal fusionpresentation attack detectionheartbeat signal |
| spellingShingle | Nassim Ammour Yakoub Bazi Naif Alajlan Multimodal Approach for Enhancing Biometric Authentication Journal of Imaging fingerprint multimodal fusion presentation attack detection heartbeat signal |
| title | Multimodal Approach for Enhancing Biometric Authentication |
| title_full | Multimodal Approach for Enhancing Biometric Authentication |
| title_fullStr | Multimodal Approach for Enhancing Biometric Authentication |
| title_full_unstemmed | Multimodal Approach for Enhancing Biometric Authentication |
| title_short | Multimodal Approach for Enhancing Biometric Authentication |
| title_sort | multimodal approach for enhancing biometric authentication |
| topic | fingerprint multimodal fusion presentation attack detection heartbeat signal |
| url | https://www.mdpi.com/2313-433X/9/9/168 |
| work_keys_str_mv | AT nassimammour multimodalapproachforenhancingbiometricauthentication AT yakoubbazi multimodalapproachforenhancingbiometricauthentication AT naifalajlan multimodalapproachforenhancingbiometricauthentication |