A KCP-DCNN-Based Two-Step Verification Multimodal Biometric Authentication System featuring QR Code Fabrication

A KCP-DCNN-Based Two-Step Verification Multimodal Biometric Authentication System featuring QR Code Fabrication | 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 A KCP-DCNN-Based Two-Step Verification Multimodal Biometric Authentication System featuring QR Code Fabrication Jananee Vinayagam, Golda Dilip This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4267404/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 Starting with for, need change Enhanced authentication performance, the concept of multi-biometrics authentication systems has emerged as a promising solution in today's digital era. In existing literature, numerous studies on multi-biometrics authentication have been carried out. However, such studies have proven their inefficiency in combining biometric and non-biometric for authentication and differentiating real and forged biometric data. Thus, an effective multimodal Biometric Authentication (BA) technique utilizing a Kernel Correlation Padding-based Deep Convolutional Neural Network (KCP-DCNN) is proposed in this paper. In the model, signature, fingerprint, and face modalities are combined. Primarily, the input images are preprocessed for image magnification utilizing the Radial Basis Function-centric Pixel Replication Technique (RBF-PRT) and augmentation utilizing Log Z-Score-centric Generative Adversarial Networks (LZS-GAN). Next, for FDivergenceAdaFactor-centric Snake Active Contour Model (FDAF-SACM) based contour extraction, Chaincode-centric minutia extraction, and Dlib's 68-centric facial point extraction, the magnified signature, magnified fingerprint, and augmented face images are utilized need combine with first part presented in the abstract. In this digital age, multi-biometric authentication systems have become a potential approach for improving authentication performance. Existing literature elaborates numerous studies on multi-biometrics authentication have been carried out. However, such studies have proven their inefficiency in combining biometric and non-biometric for authentication and differentiating real and forged biometric data. Thus, an effective multimodal Biometric Authentication (BA) technique utilizing a Kernel Correlation Padding-based Deep Convolutional Neural Network (KCP-DCNN) is proposed in this paper. In the model, signature, fingerprint, and face modalities are combined. Primarily, the input images are preprocessed for image magnification utilizing the Radial Basis Function-centric Pixel Replication Technique (RBF-PRT) and augmentation utilizing Log Z-Score-centric Generative Adversarial Networks (LZS-GAN). Next, for FDivergence AdaFactor-centric Snake Active Contour Model (FDAF-SACM) based contour extraction, Chaincode-centric minutia extraction, and Dlib's 68-centric facial point extraction, the magnified signature, magnified fingerprint, and augmented face images are utilized. Proposed technique augmented its precision, recall, and F-measure1.88%, 2.47%, and 1.19% than the prevailing CNN.Then, for efficient classification utilizing KCP-DCNN, significant features are extracted. If the classification output is real, then the user is authenticated after the verification of the Quick Response (QR) code generated utilizing the extracted points. The user identity is recognized with 98.181% accuracy by the developed model. Thus, the authentication rate of the Multimodal Biometric (MB) system is increased 98.8% accuracywhat percentage? by the proposed system. move this first part of the abstract.Then, for efficient classification utilizing KCP-DCNN, significant features are extracted. If the classification output is real, then the user is authenticated after the verification of the Quick Response (QR) code generated utilizing the extracted points. Thus, the authentication rate of the Multimodal Biometric (MB) system is increased by the proposed system. Log Z-Score-based Generative Adversarial Networks (LZS-GAN) Radial Basis Function-based Pixel Replication Technique (RBF-PRT) Kernel Correlation Padding based Deep Convolutional Neural Network (KCP-DCNN) FDivergence AdaFactor based Snake Active Contour Model (FDAF-SACM) face fingerprint signature and QR code generation. 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-4267404","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292461058,"identity":"eb80dff3-1066-4379-bc17-56bdcd3510e4","order_by":0,"name":"Jananee Vinayagam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYFACNgYDEMUPIhIKSNEi2QDSYkCkFjAwOAAmidAg334soeDnDrs84/OrEz88MGCQ5xc7gF+LwZm0A4a9Z5KLzW683SwBdJjhzNkJBLQwpDcY8LYxJ267cXYDSEuCwW0CWuT7nzcY/m2rT9w84+zmH0RpYbiRdsCYt+1w4gb+3m3E2WJw41mCsWzb8cQZN3i3WSQYSBD2i3x/mpnh27bqxP7+s5tv/qiwkeeXJuQwYMRAIkMCrFKCoHIQYH4ApvgPEKV6FIyCUTAKRiAAAH1dRvXFupORAAAAAElFTkSuQmCC","orcid":"","institution":"SRM University","correspondingAuthor":true,"prefix":"","firstName":"Jananee","middleName":"","lastName":"Vinayagam","suffix":""},{"id":292461059,"identity":"406c62fb-54ad-4743-a451-f3b057b4cd29","order_by":1,"name":"Golda Dilip","email":"","orcid":"","institution":"SRM University","correspondingAuthor":false,"prefix":"","firstName":"Golda","middleName":"","lastName":"Dilip","suffix":""}],"badges":[],"createdAt":"2024-04-15 05:43:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4267404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4267404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60689995,"identity":"6de59f02-7cc3-4bcf-9ce5-fda524cf6ada","added_by":"auto","created_at":"2024-07-19 14:47:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632630,"visible":true,"origin":"","legend":"","description":"","filename":"SCIPAPERWORKAuthenticationversionNEW1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4267404/v1_covered_d45e1155-1fbe-41ee-a8fd-4f7e50ec135e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A KCP-DCNN-Based Two-Step Verification Multimodal Biometric Authentication System featuring QR Code Fabrication","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":"Log Z-Score-based Generative Adversarial Networks (LZS-GAN), Radial Basis Function-based Pixel Replication Technique (RBF-PRT), Kernel Correlation Padding based Deep Convolutional Neural Network (KCP-DCNN), FDivergence AdaFactor based Snake Active Contour Model (FDAF-SACM), face, fingerprint, signature, and QR code generation.","lastPublishedDoi":"10.21203/rs.3.rs-4267404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4267404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStarting with for, need change Enhanced authentication performance, the concept of multi-biometrics authentication systems has emerged as a promising solution in today's digital era. In existing literature, numerous studies on multi-biometrics authentication have been carried out. However, such studies have proven their inefficiency in combining biometric and non-biometric for authentication and differentiating real and forged biometric data. Thus, an effective multimodal Biometric Authentication (BA) technique utilizing a Kernel Correlation Padding-based Deep Convolutional Neural Network (KCP-DCNN) is proposed in this paper. In the model, signature, fingerprint, and face modalities are combined. Primarily, the input images are preprocessed for image magnification utilizing the Radial Basis Function-centric Pixel Replication Technique (RBF-PRT) and augmentation utilizing Log Z-Score-centric Generative Adversarial Networks (LZS-GAN). Next, for FDivergenceAdaFactor-centric Snake Active Contour Model (FDAF-SACM) based contour extraction, Chaincode-centric minutia extraction, and Dlib's 68-centric facial point extraction, the magnified signature, magnified fingerprint, and augmented face images are utilized need combine with first part presented in the abstract.\u003c/p\u003e\n\u003cp\u003eIn this digital age, multi-biometric authentication systems have become a potential approach for improving authentication performance. Existing literature elaborates numerous studies on multi-biometrics authentication have been carried out. However, such studies have proven their inefficiency in combining biometric and non-biometric for authentication and differentiating real and forged biometric data. Thus, an effective multimodal Biometric Authentication (BA) technique utilizing a Kernel Correlation Padding-based Deep Convolutional Neural Network (KCP-DCNN) is proposed in this paper. In the model, signature, fingerprint, and face modalities are combined. Primarily, the input images are preprocessed for image magnification utilizing the Radial Basis Function-centric Pixel Replication Technique (RBF-PRT) and augmentation utilizing Log Z-Score-centric Generative Adversarial Networks (LZS-GAN). Next, for FDivergence AdaFactor-centric Snake Active Contour Model (FDAF-SACM) based contour extraction, Chaincode-centric minutia extraction, and Dlib's 68-centric facial point extraction, the magnified signature, magnified fingerprint, and augmented face images are utilized. Proposed technique augmented its precision, recall, and F-measure1.88%, 2.47%, and 1.19% than the prevailing CNN.Then, for efficient classification utilizing KCP-DCNN, significant features are extracted. If the classification output is real, then the user is authenticated after the verification of the Quick Response (QR) code generated utilizing the extracted points. The user identity is recognized with 98.181% accuracy by the developed model. Thus, the authentication rate of the Multimodal Biometric (MB) system is increased 98.8% accuracywhat percentage? by the proposed system. move this first part of the abstract.Then, for efficient classification utilizing KCP-DCNN, significant features are extracted. If the classification output is real, then the user is authenticated after the verification of the Quick Response (QR) code generated utilizing the extracted points. Thus, the authentication rate of the Multimodal Biometric (MB) system is increased by the proposed system.\u003c/p\u003e","manuscriptTitle":"A KCP-DCNN-Based Two-Step Verification Multimodal Biometric Authentication System featuring QR Code Fabrication","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 04:28:02","doi":"10.21203/rs.3.rs-4267404/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a095b916-8b34-4fd8-a2b1-4109dcc824c1","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-19T14:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 04:28:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4267404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4267404","identity":"rs-4267404","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}