Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks

Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks | 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 Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks Rami Haffar, David Sánchez, Josep Domingo-Ferrer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5281659/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 Human facial data offer valuable potential for tackling classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. However, recent privacy regulations, particularly the EU General Data Protection Regulation, have restricted the collection and usage of human images in research. As a result, several previously published face data sets have been removed from the internet due to inadequate data collection methods and privacy concerns. While synthetic data sets have been suggested as an alternative, they fall short of accurately representing the real data distribution. Additionally, most existing data sets are labeled for just a single task, which limits their versatility. To address these limitations, we introduce the Multi-Task Face (MTF) data set, designed for various tasks including face recognition and classification by race, gender, and age, as well as for aiding in training generative networks. The MTF data set comes in two versions: a non-curated set containing 132,816 images of 640 individuals, and a manually curated set with 5,246 images of 240 individuals, meticulously selected to maximize their classification quality. Both data sets were ethically sourced, using publicly available celebrity images in full compliance with copyright regulations. Along with providing detailed descriptions of data collection and processing, we evaluate the MTF data set's effectiveness in training five deep learning models across the aforementioned classification tasks. Both MTF data sets can be accessed through the following link. \url{ https://github.com/RamiHaf/MTF_data_set} Face images Image data set Image classification Deep learning 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-5281659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367901812,"identity":"7c224708-9b07-4076-a088-df19dd9dded4","order_by":0,"name":"Rami Haffar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYJCCA4wNDPzsDQyMD0jSItlzgIHZAMJnJkIPVAubBFFazNtPJx5g3GEnwcN+9lk1757DDPzt5w/g1SJzJnfDAcYzyRI8POlmt3meHWaQOJOM3xYJBpCWNuY6e4Y0tts8B9IYDBgIaeF/C9JSL8HD/4ytGKyF/zEBLRJgWw5L8EiksTHzHLBhMJAgZIsE0JbEtuNALc+YJeccsOGRuPHYgIDDcjd/+NhWDXRYGuOHNwck5Pj7Ex/gtwYEEpDYPISVj4JRMApGwSggCACjh0BNb79hrgAAAABJRU5ErkJggg==","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":true,"prefix":"","firstName":"Rami","middleName":"","lastName":"Haffar","suffix":""},{"id":367901813,"identity":"2947ff4b-da4c-474a-a3b7-2fe439327cfe","order_by":1,"name":"David Sánchez","email":"","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Sánchez","suffix":""},{"id":367901814,"identity":"2ec260f1-362b-4eb2-9ff6-ffda4e7573c0","order_by":2,"name":"Josep Domingo-Ferrer","email":"","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":false,"prefix":"","firstName":"Josep","middleName":"","lastName":"Domingo-Ferrer","suffix":""}],"badges":[],"createdAt":"2024-10-17 09:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5281659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5281659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75645540,"identity":"2919f562-ab62-47d1-87f5-a376a16b654f","added_by":"auto","created_at":"2025-02-06 16:31:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1821525,"visible":true,"origin":"","legend":"","description":"","filename":"MTFdatasetSpringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5281659/v1_covered_948d6baf-89dc-450d-b07d-6204d45a9526.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks","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":"Face images, Image data set, Image classification, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-5281659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5281659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\n\nHuman facial data offer valuable potential for tackling classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. 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