Empowering artificial intelligence-based multi-biometric image sensor for human identification
Artificial intelligence (AI) and sensor technology developments have sparked revolutionary shifts in a number of fields, including biometric Identification. In order to improve human identification processes, this research offers a novel method that integrates AI and many biometric image sensors. Th...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917424000588 |
| _version_ | 1797205966857437184 |
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| author | M. Ramkumar Prabhu R. Sivaraman N. Nagabhooshanam R. Sampath Kumar Satish S. Salunkhe |
| author_facet | M. Ramkumar Prabhu R. Sivaraman N. Nagabhooshanam R. Sampath Kumar Satish S. Salunkhe |
| author_sort | M. Ramkumar Prabhu |
| collection | DOAJ |
| description | Artificial intelligence (AI) and sensor technology developments have sparked revolutionary shifts in a number of fields, including biometric Identification. In order to improve human identification processes, this research offers a novel method that integrates AI and many biometric image sensors. The accuracy, robustness, and susceptibility to spoofing assaults of conventional single-modal biometric systems are among their many drawbacks. To overcome these challenges, we introduce a secure multi-biometric system that relies on feature-level fusion to identify users. In the preprocessing step, fingerprint images undergo Min-Max normalization to mitigate variations in image quality. In order to extract high-level features from both raw Electrocardiogram (ECG) signals and Min-Max normalized fingerprint images, ResNet50, a deep convolutional neural network, is used. These extracted feature vectors are able to distinguish between the two modalities. We proposed boosted Xgboost as a classifier for authentication in the identification steps to improve performance. The proposed approach is simulated using Python. A comparison study for improved Xgboost is presented using measures for accuracy, precision-recall, and F1-Score. Across all comparative metrics, the technique achieves much better performance. According to experimental findings, the suggested multi-biometric systems are more effective, dependable, and robust than the existing multi-biometric authentication systems. |
| first_indexed | 2024-04-24T08:59:31Z |
| format | Article |
| id | doaj.art-48f437fb71b249a1bf1676a92785bafb |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-24T08:59:31Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-48f437fb71b249a1bf1676a92785bafb2024-04-16T04:09:51ZengElsevierMeasurement: Sensors2665-91742024-06-0133101082Empowering artificial intelligence-based multi-biometric image sensor for human identificationM. Ramkumar Prabhu0R. Sivaraman1N. Nagabhooshanam2R. Sampath Kumar3Satish S. Salunkhe4Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDwaraka Doss Goverdhan Doss Vaishnav College, Chennai, IndiaDepartment of Mechanical Engineering, Aditya University, Surampalem, India; Corresponding author.Department of Aeronautical Engineering, Er. Perumal Manimekalai College of Engineering, Hosur – 635117, IndiaComputer Engineering Department, Terna Engineering College, Mumbai University, IndiaArtificial intelligence (AI) and sensor technology developments have sparked revolutionary shifts in a number of fields, including biometric Identification. In order to improve human identification processes, this research offers a novel method that integrates AI and many biometric image sensors. The accuracy, robustness, and susceptibility to spoofing assaults of conventional single-modal biometric systems are among their many drawbacks. To overcome these challenges, we introduce a secure multi-biometric system that relies on feature-level fusion to identify users. In the preprocessing step, fingerprint images undergo Min-Max normalization to mitigate variations in image quality. In order to extract high-level features from both raw Electrocardiogram (ECG) signals and Min-Max normalized fingerprint images, ResNet50, a deep convolutional neural network, is used. These extracted feature vectors are able to distinguish between the two modalities. We proposed boosted Xgboost as a classifier for authentication in the identification steps to improve performance. The proposed approach is simulated using Python. A comparison study for improved Xgboost is presented using measures for accuracy, precision-recall, and F1-Score. Across all comparative metrics, the technique achieves much better performance. According to experimental findings, the suggested multi-biometric systems are more effective, dependable, and robust than the existing multi-biometric authentication systems.http://www.sciencedirect.com/science/article/pii/S2665917424000588Multi-biometric Image sensorHuman IdentificationBoosted Xgboost, Artificial intelligence (AI) |
| spellingShingle | M. Ramkumar Prabhu R. Sivaraman N. Nagabhooshanam R. Sampath Kumar Satish S. Salunkhe Empowering artificial intelligence-based multi-biometric image sensor for human identification Measurement: Sensors Multi-biometric Image sensor Human Identification Boosted Xgboost, Artificial intelligence (AI) |
| title | Empowering artificial intelligence-based multi-biometric image sensor for human identification |
| title_full | Empowering artificial intelligence-based multi-biometric image sensor for human identification |
| title_fullStr | Empowering artificial intelligence-based multi-biometric image sensor for human identification |
| title_full_unstemmed | Empowering artificial intelligence-based multi-biometric image sensor for human identification |
| title_short | Empowering artificial intelligence-based multi-biometric image sensor for human identification |
| title_sort | empowering artificial intelligence based multi biometric image sensor for human identification |
| topic | Multi-biometric Image sensor Human Identification Boosted Xgboost, Artificial intelligence (AI) |
| url | http://www.sciencedirect.com/science/article/pii/S2665917424000588 |
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