A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques
Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of mult...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sci |
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| Online Access: | https://www.mdpi.com/2413-4155/5/1/10 |
| _version_ | 1797609170040520704 |
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| author | Sandeep Pratap Singh Shamik Tiwari |
| author_facet | Sandeep Pratap Singh Shamik Tiwari |
| author_sort | Sandeep Pratap Singh |
| collection | DOAJ |
| description | Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal systems has increased the biometric system’s overall recognition rate. A new degree of fusion, known as an intelligent Dual Multimodal Biometric Authentication Scheme, is established in this study. In the proposed work, two multimodal biometric systems are developed by combining three unimodal biometric systems. ECG, sclera, and fingerprint are the unimodal systems selected for this work. The sequential model biometric system is developed using a decision-level fusion based on WOA-ANN. The parallel model biometric system is developed using a score-level fusion based on SSA-DBN. The biometric authentication performs preprocessing, feature extraction, matching, and scoring for each unimodal system. On each biometric attribute, matching scores and individual accuracy are cyphered independently. A matcher performance-based fusion procedure is demonstrated for the three biometric qualities because the matchers on these three traits produce varying values. The two-level fusion technique (score and feature) is implemented separately, and their results with the current scheme are compared to exhibit the optimum model. The suggested plan makes use of the highest TPR, FPR, and accuracy rates. |
| first_indexed | 2024-03-11T05:57:47Z |
| format | Article |
| id | doaj.art-f04aee48ec9745a0ba2d661ed7af5354 |
| institution | Directory Open Access Journal |
| issn | 2413-4155 |
| language | English |
| last_indexed | 2024-03-11T05:57:47Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sci |
| spelling | doaj.art-f04aee48ec9745a0ba2d661ed7af53542023-11-17T13:42:18ZengMDPI AGSci2413-41552023-03-01511010.3390/sci5010010A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN TechniquesSandeep Pratap Singh0Shamik Tiwari1School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, IndiaSchool of Computer Science, University of Petroleum & Energy Studies, Dehradun 248007, IndiaIdentity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal systems has increased the biometric system’s overall recognition rate. A new degree of fusion, known as an intelligent Dual Multimodal Biometric Authentication Scheme, is established in this study. In the proposed work, two multimodal biometric systems are developed by combining three unimodal biometric systems. ECG, sclera, and fingerprint are the unimodal systems selected for this work. The sequential model biometric system is developed using a decision-level fusion based on WOA-ANN. The parallel model biometric system is developed using a score-level fusion based on SSA-DBN. The biometric authentication performs preprocessing, feature extraction, matching, and scoring for each unimodal system. On each biometric attribute, matching scores and individual accuracy are cyphered independently. A matcher performance-based fusion procedure is demonstrated for the three biometric qualities because the matchers on these three traits produce varying values. The two-level fusion technique (score and feature) is implemented separately, and their results with the current scheme are compared to exhibit the optimum model. The suggested plan makes use of the highest TPR, FPR, and accuracy rates.https://www.mdpi.com/2413-4155/5/1/10multimodal biometric authentication systemelectrocardiograph (ECG)sclerafingerprintwhale-optimization-algorithm-based artificial neural network (WOA-ANN)salp-swarm-algorithm-based deep belief network (SSA-DBN) |
| spellingShingle | Sandeep Pratap Singh Shamik Tiwari A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques Sci multimodal biometric authentication system electrocardiograph (ECG) sclera fingerprint whale-optimization-algorithm-based artificial neural network (WOA-ANN) salp-swarm-algorithm-based deep belief network (SSA-DBN) |
| title | A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques |
| title_full | A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques |
| title_fullStr | A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques |
| title_full_unstemmed | A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques |
| title_short | A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques |
| title_sort | dual multimodal biometric authentication system based on woa ann and ssa dbn techniques |
| topic | multimodal biometric authentication system electrocardiograph (ECG) sclera fingerprint whale-optimization-algorithm-based artificial neural network (WOA-ANN) salp-swarm-algorithm-based deep belief network (SSA-DBN) |
| url | https://www.mdpi.com/2413-4155/5/1/10 |
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