Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/40 |
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| author | Ali Cherry Aya Nasser Wassim Salameh Mohamad Abou Ali Mohamad Hajj-Hassan |
| author_facet | Ali Cherry Aya Nasser Wassim Salameh Mohamad Abou Ali Mohamad Hajj-Hassan |
| author_sort | Ali Cherry |
| collection | DOAJ |
| description | The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication. A comprehensive protocol was established to collect PPG signals from 40 subjects using a custom-built acquisition system. These signals were then transformed into two-dimensional representations through the Gram matrix conversion technique. To analyze and authenticate users, we employed an EfficientNetV2 B0 model integrated with a Long Short-Term Memory (LSTM) network, achieving a remarkable 99% accuracy on the test set. Additionally, the model demonstrated outstanding precision, recall, and F1 scores. The refined model was further validated in real-time identification scenarios, underscoring its effectiveness and robustness for next-generation biometric recognition systems. |
| format | Article |
| id | doaj-art-0504d3da03d44c7f9b24e3913375d373 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0504d3da03d44c7f9b24e3913375d3732025-01-10T13:20:39ZengMDPI AGSensors1424-82202024-12-012514010.3390/s25010040Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning ModelsAli Cherry0Aya Nasser1Wassim Salameh2Mohamad Abou Ali3Mohamad Hajj-Hassan4Department of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, LebanonDepartment of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, LebanonDepartment of Mechanical Engineering, Lebanese International University, Beirut P.O. Box 146404, LebanonDepartment of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, LebanonDepartment of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, LebanonThe integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication. A comprehensive protocol was established to collect PPG signals from 40 subjects using a custom-built acquisition system. These signals were then transformed into two-dimensional representations through the Gram matrix conversion technique. To analyze and authenticate users, we employed an EfficientNetV2 B0 model integrated with a Long Short-Term Memory (LSTM) network, achieving a remarkable 99% accuracy on the test set. Additionally, the model demonstrated outstanding precision, recall, and F1 scores. The refined model was further validated in real-time identification scenarios, underscoring its effectiveness and robustness for next-generation biometric recognition systems.https://www.mdpi.com/1424-8220/25/1/40photoplethysmography (PPG) signalsliveness detectionbiometric securitytwo-dimensional formatGram matrix conversiondeep learning |
| spellingShingle | Ali Cherry Aya Nasser Wassim Salameh Mohamad Abou Ali Mohamad Hajj-Hassan Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models Sensors photoplethysmography (PPG) signals liveness detection biometric security two-dimensional format Gram matrix conversion deep learning |
| title | Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models |
| title_full | Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models |
| title_fullStr | Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models |
| title_full_unstemmed | Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models |
| title_short | Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models |
| title_sort | real time ppg based biometric identification advancing security with 2d gram matrices and deep learning models |
| topic | photoplethysmography (PPG) signals liveness detection biometric security two-dimensional format Gram matrix conversion deep learning |
| url | https://www.mdpi.com/1424-8220/25/1/40 |
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