Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model | 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 Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model Dharmalingam Muthusamy, Rakkimuthu Ponnusamy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2255952/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 Vein authentication is a novel biometric method to authenticate the individuality of a person. The conventional biometric technique employs shape images and exact segments of finger veins for the verification process. To improve the verification accuracy, a novel Anisotropic Filtered Stromberg Feature Transform based on Tucker’s Congruence Deep Belief Structure Learning (AFSFT-TCDBSL) technique is intended. The proposed AFSFT-TCDBSL technique comprises one input, three hidden, and one output layers. The numbers of images are collected in the input, and input images are preprocessed using anisotropic diffusion filtering in the first hidden layer. Finally, the verification process is performed using Tucker’s congruence correlation coefficient (TCCC). Based on the correlation, the verification outputs are getting to the output layer. In this way, accurate finger vein verification is performed with superior accuracy and with a minimum false rate. We performed experimental assessments with different factors, such as PSNR, FVVA, FPR, and CT. The proposed ADFSFT-TCDBSL technique offers better finger-vein verification results than the state-of-the-art methods. Finger vein verification Deep Belief Structure Learning Anisotropic Diffusion filter Stromberg wavelet transform feature extraction Tucker’s Congruence correlation coefficient feature matching 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. 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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-2255952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":151111117,"identity":"2cedacb7-0b12-456f-8c78-1c82274bf96e","order_by":0,"name":"Dharmalingam Muthusamy","email":"data:image/png;base64,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","orcid":"","institution":"Government Arts and Science College Modakkurichi","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dharmalingam","middleName":"","lastName":"Muthusamy","suffix":""},{"id":151111119,"identity":"31b9ea71-a242-4bac-91bf-6015ea740342","order_by":1,"name":"Rakkimuthu Ponnusamy","email":"","orcid":"","institution":"Government Arts and Science College Gudalur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rakkimuthu","middleName":"","lastName":"Ponnusamy","suffix":""}],"badges":[],"createdAt":"2022-11-09 15:14:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2255952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2255952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29045957,"identity":"6971368b-3ba7-4ebd-afdd-c0a2c91d5118","added_by":"auto","created_at":"2022-11-14 18:44:38","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":563764,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript2022.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2255952/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model","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":"Finger vein verification, Deep Belief Structure Learning, Anisotropic Diffusion filter, Stromberg wavelet transform, feature extraction, Tucker’s Congruence correlation coefficient feature matching","lastPublishedDoi":"10.21203/rs.3.rs-2255952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2255952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVein authentication is a novel biometric method to authenticate the individuality of a person. 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