Siamese neural network based algorithm for user recognition by their eye blinking

Siamese neural network based algorithm for user recognition by their eye blinking | 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 Siamese neural network based algorithm for user recognition by their eye blinking Kamil Malinowski, Khalid Saeed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4223725/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 The article proposes a new method for user recognition based on their unique eyelid blinking pattern. Our research aimed to develop a method that is resistant to shoulder surfing and brute force attacks, while also not requiring complex recording devices. Most user authentication methods utilizing eyelid blinking patterns are vulnerable to pattern replication attacks. On the other hand, methods using EEG sometimes require the use of complicated equipment to record the blinking event. In our study, we utilized the publicly available mEBAL database. The temporal eyelid movement patterns extracted from the samples in the database are analyzed by a Siamese neural network. The achieved results of 98.20% accuracy and 0.11 EER unequivocally demonstrate the superiority of the proposed method over other methods using eyelid blinking for user authentication. Biometrics Iris Eye blinking Passwords 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-4223725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290700426,"identity":"c4fdc83f-99c9-4720-a2ba-3ffb4bec6858","order_by":0,"name":"Kamil Malinowski","email":"data:image/png;base64,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","orcid":"","institution":"Bialystok University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Kamil","middleName":"","lastName":"Malinowski","suffix":""},{"id":290700427,"identity":"09f4d421-ff79-4268-8e44-24ace142d26c","order_by":1,"name":"Khalid Saeed","email":"","orcid":"","institution":"University of the Coast","correspondingAuthor":false,"prefix":"","firstName":"Khalid","middleName":"","lastName":"Saeed","suffix":""}],"badges":[],"createdAt":"2024-04-05 15:00:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4223725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4223725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56808840,"identity":"c212423d-8d82-4a04-ae2b-8af05222e25a","added_by":"auto","created_at":"2024-05-20 18:40:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":567962,"visible":true,"origin":"","legend":"","description":"","filename":"Siameseneuralnetworkbasedalgorithmforuserrecognitionbytheireyeblinking.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4223725/v1_covered_19f151dc-1b64-4f84-8b12-0062abb1f967.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Siamese neural network based algorithm for user recognition by their eye blinking","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":"Biometrics, Iris, Eye blinking, Passwords","lastPublishedDoi":"10.21203/rs.3.rs-4223725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4223725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe article proposes a new method for user recognition based on their unique eyelid blinking pattern. 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