Design a Multimodal Biometric Based Protection System by Generation of a Revocable Cryptographic Key Using Separately Extracted Feature Fusion-based Convolutional Neural Network With Bat Optimization

Design a Multimodal Biometric Based Protection System by Generation of a Revocable Cryptographic Key Using Separately Extracted Feature Fusion-based Convolutional Neural Network With Bat Optimization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design a Multimodal Biometric Based Protection System by Generation of a Revocable Cryptographic Key Using Separately Extracted Feature Fusion-based Convolutional Neural Network With Bat Optimization Manjusha Nair S, Smitha Dharan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4853162/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract An authentication and access control method based on the automatic and immediate verification of a person's bodily traits is known as biometric security. Recently, Biometric Identification Systems (BIS) demands have risen significantly. However, it is challenging to offer robust security quickly because most conventional single and multimodal biometric authentication systems utilize data size, static parameters, and keys. As a result, it has various issues, including noise in the data, unacceptable error rates, and spoof attacks. To overcome these issues, this paper designs a separately extracted feature fusion based convolutional neural network with bat optimization (SEFF-CNN-BO). Additionally, the Attribute-Based Encryption (ABE) technique is employed to securely share the generated cryptographic key. Moreover, Bat Optimization and user input string-based permutation followed by SHA-256 hash value generation is applied to the extracted biometric features in the key generation phase to ensure key revocability and to enhance the security of the biometric system. This SEFF-CNN-BO system has combined biological features from the iris, face, and fingerprint for individual identification. The performance of SEFF-CNN-BO was calculated and evaluated in terms of precision, accuracy, recall, specificity, sensitivity, F-Score, etc. Compared to previous models, the developed SEFF-CNN-BO model attained 99.56% accuracy and 99.64% recall respectively. A comprehensive analysis of the proposed model against different security attacks, key revocability, and evaluation of the strength of the generated cryptographic keys is done using various metrics available in NIST statistical test suite. Analysis shows that the method is secure against all known attacks and also the key is 100% revocable. Biometric Protection System Cryptographic Key Bat Optimization Multi-Model Biometrics Attribute-Based Encryption SHA-256 Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4853162","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338873006,"identity":"2af3f3c5-75e3-453b-9927-3b9da554ca9c","order_by":0,"name":"Manjusha Nair S","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYLACCQaGBBB9gKECSDIzN5Ci5QxICyMRWhigWhgY28Akfi3y0w4/e2DZVpfHP7v54eHKebXR/O1ALT8qtuHUYnA7zdxAsu1wscSdYwYHz247njvjMGMDY8+Z27i1SCeYSUi2HUhsuJFgcLBx27HcBqAWZsY23FrkZ6d/A2qpS5x/I/3DwcY5x3LnE9LCcDsHZAtz4oYbOUBbGmpyNxDSYnA7p0xC4tzhxI03cgoONhw7kLsRqOUgPr8AHbZNWqKsLnHejfTNHxtq6nLnnT988MGPCjwOAwJmCQT7MJg8gFc9EDB+QLDrCCkeBaNgFIyCEQgAwwpjNpCdED4AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Manjusha","middleName":"Nair","lastName":"S","suffix":""},{"id":338873008,"identity":"e907212a-42d1-4cae-aedd-e103ec540813","order_by":1,"name":"Smitha Dharan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Smitha","middleName":"","lastName":"Dharan","suffix":""}],"badges":[],"createdAt":"2024-08-03 11:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4853162/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4853162/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64526140,"identity":"f252e530-75d1-4f4a-994d-d9eea632c2ac","added_by":"auto","created_at":"2024-09-14 13:41:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":920077,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4853162/v1_covered_1583c9fb-5110-4870-91d0-3ad0d877ea8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDesign a Multimodal Biometric Based Protection System by Generation of a Revocable Cryptographic Key Using Separately Extracted Feature Fusion-based Convolutional Neural Network With Bat Optimization\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biometric Protection System, Cryptographic Key, Bat Optimization, Multi-Model Biometrics, Attribute-Based Encryption, SHA-256, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-4853162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4853162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn authentication and access control method based on the automatic and immediate verification of a person's bodily traits is known as biometric security. 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