Secure Face Authentication using Sentiment Analysis and Adaptive Fuzzy Inference system

Secure Face Authentication using Sentiment Analysis and Adaptive Fuzzy Inference system | 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 Secure Face Authentication using Sentiment Analysis and Adaptive Fuzzy Inference system Suruchi Chawla, Aakanksha Vats, Radhika Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9359369/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 To secure access control, one of the most popular method is Face authentication. These existing methods are not very robust against changes in emotion; and they have demonstrated vulnerability to spoofing attacks and external (e.g. environmental) variation because they use static recognition. Static recognition is the basis for most current technologies used for facial authentication, and they only utilize simple liveliness tests. To address these limitations, a hybrid framework is proposed for secure face authentication that combines both real-time sentiment analysis with an adaptive fuzzy inference system (FIS). The adaptive FIS will improve over time as more users enroll, thus allowing for smarter and more informed contextual (i.e. smart ) decisions. This hybrid system achieved ~ 94% accuracy rate. The results show how effectively the framework counteracts spoofing attacks, display high levels of robustness against emotional variability, and react to real-world environmental disturbances. Facial Recognition Sentiment Analysis Fuzzy inference system (FIS) Adaptive Fuzzy Inference Encrypted Biometric Templates Support Vector Machines Real-time Authentication 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-9359369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641621967,"identity":"5c852837-e158-44af-9d76-eb5191c6cef9","order_by":0,"name":"Suruchi Chawla","email":"","orcid":"","institution":"Shaheed Rajguru College of Applied Sciences for Women","correspondingAuthor":false,"prefix":"","firstName":"Suruchi","middleName":"","lastName":"Chawla","suffix":""},{"id":641621969,"identity":"ec0dc5c7-5780-49ec-8e5b-d0b97b6a733b","order_by":1,"name":"Aakanksha Vats","email":"data:image/png;base64,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","orcid":"","institution":"Shaheed Rajguru College of Applied Sciences for Women","correspondingAuthor":true,"prefix":"","firstName":"Aakanksha","middleName":"","lastName":"Vats","suffix":""},{"id":641621972,"identity":"f8353dff-1ddb-4647-a6df-ec2cab657b43","order_by":2,"name":"Radhika Sharma","email":"","orcid":"","institution":"Shaheed Rajguru College of Applied Sciences for Women","correspondingAuthor":false,"prefix":"","firstName":"Radhika","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2026-04-08 16:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9359369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9359369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109761051,"identity":"569c4ecc-34fd-4ba7-8089-161ae0efe48d","added_by":"auto","created_at":"2026-05-22 07:29:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":662994,"visible":true,"origin":"","legend":"","description":"","filename":"FinalPaperRevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9359369/v1_covered_b5a1dfb5-9a35-4d58-aa7e-c683ab87d9da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Secure Face Authentication using Sentiment Analysis and Adaptive Fuzzy Inference system","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":"Facial Recognition, Sentiment Analysis, Fuzzy inference system (FIS), Adaptive Fuzzy Inference, Encrypted Biometric Templates, Support Vector Machines, Real-time Authentication","lastPublishedDoi":"10.21203/rs.3.rs-9359369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9359369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo secure access control, one of the most popular method is Face authentication. These existing methods are not very robust against changes in emotion; and they have demonstrated vulnerability to spoofing attacks and external (e.g. environmental) variation because they use static recognition. Static recognition is the basis for most current technologies used for facial authentication, and they only utilize simple liveliness tests. To address these limitations, a hybrid framework is proposed for secure face authentication that combines both real-time sentiment analysis with an adaptive fuzzy inference system (FIS). The adaptive FIS will improve over time as more users enroll, thus allowing for smarter and more informed contextual (i.e. smart ) decisions. This hybrid system achieved\u0026thinsp;~\u0026thinsp;94% accuracy rate. 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