Real-time Facial Recognition Using Multi-Task Learning on a Raspberry Pi

Real-time Facial Recognition Using Multi-Task Learning on a Raspberry Pi | 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 Real-time Facial Recognition Using Multi-Task Learning on a Raspberry Pi Abdulatif Ahmed Ali ABOLUHOM, İsmet KANDİLLİ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4635596/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 This paper investigates multi-task learning for facial recognition using the Raspberry Pi, a popular single-board computer, to demonstrate how this inexpensive platform can perform deep learning tasks complexity in real time. We used MobileNet, MobileNetV2, and InceptionV3 as base models due to their efficiency and accuracy. The MTL models training were performed on a database built from photos of known individuals and celebrities from the VGGFace2 dataset, divided into three tasks: identifying individuals (9 classes), age estimation (3 groups), and ethnicity prediction (3 groups). Multitask learning enables the simultaneous execution of these tasks using shared layers between deep learning models. The results show a high accuracy rate: MTL InceptionV3 models achieved 93.3% person identification, 95.6% age estimation, and 97.5% ethnicity prediction. The MTL MobileNet model achieved the highest accuracy with 99% person identification, 99.3% age estimation, and 99.5% ethnicity prediction. The MTL MobileNetV2 model achieved 98.3% results in person identification, 97.3% in age estimation, and 99% in ethnicity prediction. These results demonstrate the significant potential of Raspberry Pi-based facial recognition systems in real-world applications such as security systems, personalized customer experiences, and demographic analytics. This study shows that multitask learning on the Raspberry Pi is practical, demonstrating that complex deep learning models can run efficiently even with limited resources. This opens opportunities for innovation, making facial recognition systems more flexible and easier to use. It also enables better resource utilization, thereby reducing computational load and energy consumption in real-time applications. Multi-task learning Raspberry Pi Deep learning Face recognition Real-time 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-4635596","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322512089,"identity":"46b72110-5572-4ff2-a0fe-4a6d518d675c","order_by":0,"name":"Abdulatif Ahmed Ali ABOLUHOM","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3RvUrEQBDA8Q0Dc82YtAkruVfYEBAPD32VSGAtba8QFA6S5rS28GFyDMTyWsEmNlYW2hwpRN2NdsdG7Cz2X2yx8GM/Rgif7x8WQdQ1RR8DTqphYx/NEo+RpKageVvNJyG1wwb9EOUkakOwvkUdpbH+JuI3InivYSKWmDyfdv3FnEIB60cS7+cuESzDgumQc5Sas1WrzcWwPCKhZlcOAiCUPaVEeVZJgWwIHUhDnDfDgSBfVsm9IR+fhkTbUUJAyj4fMMZWBlVjT8FRYgZS2E8GJF1m1zclIWA+u1O5k5xsmF/tKKd1m3X99jid1sunh5dFOvLLO4Fd/gJ8Pp/Pt9MXEPlGOaflfQkAAAAASUVORK5CYII=","orcid":"","institution":"Kocaeli University","correspondingAuthor":true,"prefix":"","firstName":"Abdulatif","middleName":"Ahmed Ali","lastName":"ABOLUHOM","suffix":""},{"id":322512091,"identity":"701ca59f-3878-4832-afdd-06e0a6df474d","order_by":1,"name":"İsmet KANDİLLİ","email":"","orcid":"","institution":"Kocaeli University","correspondingAuthor":false,"prefix":"","firstName":"İsmet","middleName":"","lastName":"KANDİLLİ","suffix":""}],"badges":[],"createdAt":"2024-06-25 10:15:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4635596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4635596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60945969,"identity":"afebd20c-9fa3-4891-a9d1-16f20f3b83f3","added_by":"auto","created_at":"2024-07-23 22:46:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":754133,"visible":true,"origin":"","legend":"","description":"","filename":"thepaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4635596/v1_covered_992969a2-2525-4422-b1e4-48b1b25272e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-time Facial Recognition Using Multi-Task Learning on a Raspberry Pi","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":"Multi-task learning, Raspberry Pi, Deep learning, Face recognition, Real-time","lastPublishedDoi":"10.21203/rs.3.rs-4635596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4635596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates multi-task learning for facial recognition using the Raspberry Pi, a popular single-board computer, to demonstrate how this inexpensive platform can perform deep learning tasks complexity in real time. We used MobileNet, MobileNetV2, and InceptionV3 as base models due to their efficiency and accuracy. The MTL models training were performed on a database built from photos of known individuals and celebrities from the VGGFace2 dataset, divided into three tasks: identifying individuals (9 classes), age estimation (3 groups), and ethnicity prediction (3 groups). Multitask learning enables the simultaneous execution of these tasks using shared layers between deep learning models. The results show a high accuracy rate: MTL InceptionV3 models achieved 93.3% person identification, 95.6% age estimation, and 97.5% ethnicity prediction. The MTL MobileNet model achieved the highest accuracy with 99% person identification, 99.3% age estimation, and 99.5% ethnicity prediction. The MTL MobileNetV2 model achieved 98.3% results in person identification, 97.3% in age estimation, and 99% in ethnicity prediction. These results demonstrate the significant potential of Raspberry Pi-based facial recognition systems in real-world applications such as security systems, personalized customer experiences, and demographic analytics. This study shows that multitask learning on the Raspberry Pi is practical, demonstrating that complex deep learning models can run efficiently even with limited resources. This opens opportunities for innovation, making facial recognition systems more flexible and easier to use. It also enables better resource utilization, thereby reducing computational load and energy consumption in real-time applications.\u003c/p\u003e","manuscriptTitle":"Real-time Facial Recognition Using Multi-Task Learning on a Raspberry Pi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 03:54:55","doi":"10.21203/rs.3.rs-4635596/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":"ee18ce8e-b0b7-4574-9b43-58d32599c61e","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T22:38:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-17 03:54:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4635596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4635596","identity":"rs-4635596","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}