Multi-biometric fusion for enhanced human authentication in information security
In the digital age, information security has taken on greater importance, necessitating the development of strong and trustworthy human authentication techniques. Traditional single-factor authentication schemes, such passwords or PINs, are prone to a number of security flaws. Multi-biometric fusion...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423003094 |
| _version_ | 1827373947337834496 |
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| author | Vipul Vekariya Manish Joshi Sukanya Dikshit S.K. Manju bargavi |
| author_facet | Vipul Vekariya Manish Joshi Sukanya Dikshit S.K. Manju bargavi |
| author_sort | Vipul Vekariya |
| collection | DOAJ |
| description | In the digital age, information security has taken on greater importance, necessitating the development of strong and trustworthy human authentication techniques. Traditional single-factor authentication schemes, such passwords or PINs, are prone to a number of security flaws. Multi-biometric fusion approaches have become a possible remedy to these restrictions. In comparison to other fusion techniques, feature level fusion is a very popular technique. Features are taken from every biometric attribute in this fusion. Then, the retrieved features are concatenated to create a high dimension final feature vector. In this study, we present a novel method for feature-level fusion employing optimal feature level fusion. The essential features are chosen and identified using a Binary chimp optimized adaptive kernel support vector machine (BCO-AKSVM). According to f1-score (99 %), recall (98 %), accuracy (97 %), precision (88 %), and time computation (1000 ms), the experimental results of the suggested method are analyzed. When compared to current methods, it can show that the proposed BCO-AKSVM achieves the highest performance of information security in human biometric authentication. Multi-biometric fusion is anticipated to play a significant role as technology develops in maintaining secure access control and safeguarding sensitive information in numerous sectors. |
| first_indexed | 2024-03-08T11:25:13Z |
| format | Article |
| id | doaj.art-f016d0e61c044ce79937c622e5b112cc |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-08T11:25:13Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-f016d0e61c044ce79937c622e5b112cc2024-01-26T05:34:52ZengElsevierMeasurement: Sensors2665-91742024-02-0131100973Multi-biometric fusion for enhanced human authentication in information securityVipul Vekariya0Manish Joshi1Sukanya Dikshit2S.K. Manju bargavi3Department of Computer Science and Engineering, Faculty of Engineering and Technology, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India; Corresponding author.Department of College of Computing Sciences and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, IndiaISME - School of Management & Entrepreneurship, ATLAS SkillTech University, Mumbai, Maharashtra, IndiaDepartment of CSIT, School of CS and IT, Jain (deemed to be) University, Karnataka, 562112, IndiaIn the digital age, information security has taken on greater importance, necessitating the development of strong and trustworthy human authentication techniques. Traditional single-factor authentication schemes, such passwords or PINs, are prone to a number of security flaws. Multi-biometric fusion approaches have become a possible remedy to these restrictions. In comparison to other fusion techniques, feature level fusion is a very popular technique. Features are taken from every biometric attribute in this fusion. Then, the retrieved features are concatenated to create a high dimension final feature vector. In this study, we present a novel method for feature-level fusion employing optimal feature level fusion. The essential features are chosen and identified using a Binary chimp optimized adaptive kernel support vector machine (BCO-AKSVM). According to f1-score (99 %), recall (98 %), accuracy (97 %), precision (88 %), and time computation (1000 ms), the experimental results of the suggested method are analyzed. When compared to current methods, it can show that the proposed BCO-AKSVM achieves the highest performance of information security in human biometric authentication. Multi-biometric fusion is anticipated to play a significant role as technology develops in maintaining secure access control and safeguarding sensitive information in numerous sectors.http://www.sciencedirect.com/science/article/pii/S2665917423003094Multi biometricAuthenticationFusionBinary chimp optimized adaptive kernel support vector machine (BCO-AKSVM) |
| spellingShingle | Vipul Vekariya Manish Joshi Sukanya Dikshit S.K. Manju bargavi Multi-biometric fusion for enhanced human authentication in information security Measurement: Sensors Multi biometric Authentication Fusion Binary chimp optimized adaptive kernel support vector machine (BCO-AKSVM) |
| title | Multi-biometric fusion for enhanced human authentication in information security |
| title_full | Multi-biometric fusion for enhanced human authentication in information security |
| title_fullStr | Multi-biometric fusion for enhanced human authentication in information security |
| title_full_unstemmed | Multi-biometric fusion for enhanced human authentication in information security |
| title_short | Multi-biometric fusion for enhanced human authentication in information security |
| title_sort | multi biometric fusion for enhanced human authentication in information security |
| topic | Multi biometric Authentication Fusion Binary chimp optimized adaptive kernel support vector machine (BCO-AKSVM) |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423003094 |
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