Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition
This article introduces a hybrid multi-biometric system incorporating fingerprint, face, and iris recognition to enhance individual authentication. The system addresses limitations of uni-modal approaches by combining multiple biometric modalities, exhibiting superior performance and heightened secu...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-02-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2699.pdf |
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| author | Sonal Ajit Singh Chander Kant |
| author_facet | Sonal Ajit Singh Chander Kant |
| author_sort | Sonal |
| collection | DOAJ |
| description | This article introduces a hybrid multi-biometric system incorporating fingerprint, face, and iris recognition to enhance individual authentication. The system addresses limitations of uni-modal approaches by combining multiple biometric modalities, exhibiting superior performance and heightened security in practical scenarios, making it more dependable and resilient for real-world applications. The integration of support vector machine (SVM) and random forest (RF) classifiers, along with optimization techniques like bacterial foraging optimization (BFO) and genetic algorithms (GA), improves efficiency and robustness. Additionally, integrating feature-level fusion and utilizing methods such as Gabor filters for feature extraction enhances overall performance of the model. The system demonstrates superior accuracy and reliability, making it suitable for real-world applications requiring secure and dependable identification solutions. |
| format | Article |
| id | doaj-art-4c03e3f634d549829dac4f4427d0ecb8 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-4c03e3f634d549829dac4f4427d0ecb82025-08-20T03:11:46ZengPeerJ Inc.PeerJ Computer Science2376-59922025-02-0111e269910.7717/peerj-cs.2699Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognitionSonal0Ajit Singh1Chander Kant2Department of Computer Science & Engineering and Information Technology, Uttarakhand Technical University, Dehradun, Uttarakhand, IndiaDepartment of Computer Science & Engineering, Bipin Tripathi Kumaon Institute of Technology, Almora, Uttarakhand, IndiaDepartment of Computer Science & Applications, Kurukshetra University, Kurukshetra, IndiaThis article introduces a hybrid multi-biometric system incorporating fingerprint, face, and iris recognition to enhance individual authentication. The system addresses limitations of uni-modal approaches by combining multiple biometric modalities, exhibiting superior performance and heightened security in practical scenarios, making it more dependable and resilient for real-world applications. The integration of support vector machine (SVM) and random forest (RF) classifiers, along with optimization techniques like bacterial foraging optimization (BFO) and genetic algorithms (GA), improves efficiency and robustness. Additionally, integrating feature-level fusion and utilizing methods such as Gabor filters for feature extraction enhances overall performance of the model. The system demonstrates superior accuracy and reliability, making it suitable for real-world applications requiring secure and dependable identification solutions.https://peerj.com/articles/cs-2699.pdfMulti-biometricIris recognitionFace authenticationFingerprint authenticationMachine learningSupport vector machine |
| spellingShingle | Sonal Ajit Singh Chander Kant Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition PeerJ Computer Science Multi-biometric Iris recognition Face authentication Fingerprint authentication Machine learning Support vector machine |
| title | Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition |
| title_full | Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition |
| title_fullStr | Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition |
| title_full_unstemmed | Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition |
| title_short | Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition |
| title_sort | optimized hybrid svm rf multi biometric framework for enhanced authentication using fingerprint iris and face recognition |
| topic | Multi-biometric Iris recognition Face authentication Fingerprint authentication Machine learning Support vector machine |
| url | https://peerj.com/articles/cs-2699.pdf |
| work_keys_str_mv | AT sonal optimizedhybridsvmrfmultibiometricframeworkforenhancedauthenticationusingfingerprintirisandfacerecognition AT ajitsingh optimizedhybridsvmrfmultibiometricframeworkforenhancedauthenticationusingfingerprintirisandfacerecognition AT chanderkant optimizedhybridsvmrfmultibiometricframeworkforenhancedauthenticationusingfingerprintirisandfacerecognition |