Reconstructing Masked Faces using Variational Quantized Variational Auto Encoders and Recognition using DCNN-ELM Hybrid Framework

Reconstructing Masked Faces using Variational Quantized Variational Auto Encoders and Recognition using DCNN-ELM Hybrid Framework | 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 Reconstructing Masked Faces using Variational Quantized Variational Auto Encoders and Recognition using DCNN-ELM Hybrid Framework Chandni Agarwal, Charul Bhatnagar, Anurag Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3949141/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 reconstruction of the face has historically been a significant issue in medical and forensic science. The presence of COVID-19 has added a significant new dimension. To model a new face, plastic surgery and informatics are employed, representing cyber forensics with challenges. The classic facial recognition techniques suffer from major drawbacks when face masks are widely used. As a result, new techniques are now being tried and tested to reconstruct a face from a collection of masked facial images. To determine the identification accuracy and other parameters/metrics, these faces are compared to real-world images of the same subject. Our research focuses on the task of post-mask face reconstruction, addressing the pressing need for precise and reliable techniques. We evaluate the effectiveness of three key algorithms: Edge Connect, Gated Convolution, and Hierarchical Variational Vector Quantized Autoencoders (HVQVAE). We use two synthetic datasets, MaskedFace-CelebA and MaskedFace-CelebAHQ, to rigorously assess the quality of reconstructed faces using metrics such as PSNR, SSIM, UIQI, and NCORR. Gated Convolution (GC) emerges as the superior choice in terms of image quality. To validate our findings, we employ five classifiers (Vgg16, Vgg19, ResNet50, ResNet101, ResNET152) and explore Extreme Learning Machine (ELM) and Support Vector Machine (SVM) as novel approaches for face recognition. A comprehensive ablation study reinforces our conclusion that Generative Convolution (GC) excels among the three models. Our research offers valuable insights into face reconstruction amid widespread mask usage, emphasizing innovative methodologies to address contemporary challenges in the field. Image In-painting GAN Auto-encoders Deep Learning Face Reconstruction Face Recognition 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-3949141","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272551840,"identity":"254b65e8-7ce3-469e-aae0-4960077056c9","order_by":0,"name":"Chandni Agarwal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACCSDmYZDg4Zd/fPABiM1HrBYZyYa0ZAMQm41ILQw2BgdyzEBsBoJa+Gc3P3vwdocFj8GBA2aVX3PsZNgYmB8+uoHPkjvHzA3nnpHgkTzYkHZbdlsy0GFsxsY5eLQYSCSYSfO2SfDwHWY4dltyGzNQCw+bNH4t6d/AWhiOMbYVS26rJ0ZLDsQWgTPMbIwftx0mrEXiRk6Z5FygFskZbMzSjNuO87AxE/AL/4z0bRJv2+rs+SX4P378ua3anp+9+eFjfFpQADMPmCRWOQgw/iBF9SgYBaNgFIwYAAA4uT9+rrIRDwAAAABJRU5ErkJggg==","orcid":"","institution":"GLA University","correspondingAuthor":true,"prefix":"","firstName":"Chandni","middleName":"","lastName":"Agarwal","suffix":""},{"id":272551841,"identity":"3f1e0ab4-f551-43b9-949e-11d22677f4bf","order_by":1,"name":"Charul Bhatnagar","email":"","orcid":"","institution":"GLA University","correspondingAuthor":false,"prefix":"","firstName":"Charul","middleName":"","lastName":"Bhatnagar","suffix":""},{"id":272551842,"identity":"29224944-8372-4e20-bab0-5533d7a0f461","order_by":2,"name":"Anurag Mishra","email":"","orcid":"","institution":"DeenDayal Upadhayay College, Delhi University","correspondingAuthor":false,"prefix":"","firstName":"Anurag","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2024-02-11 18:44:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3949141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3949141/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77509872,"identity":"c91b2915-7fec-4941-9261-c1880e00ebf4","added_by":"auto","created_at":"2025-03-02 03:38:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1047729,"visible":true,"origin":"","legend":"","description":"","filename":"facereconstGANspringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3949141/v1_covered_c793e959-1b5a-44af-8331-f37e43db5b4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reconstructing Masked Faces using Variational Quantized Variational Auto Encoders and Recognition using DCNN-ELM Hybrid Framework","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":"Image In-painting, GAN, Auto-encoders, Deep Learning, Face Reconstruction, Face Recognition","lastPublishedDoi":"10.21203/rs.3.rs-3949141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3949141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe reconstruction of the face has historically been a significant issue in medical and forensic science. The presence of COVID-19 has added a significant new dimension. To model a new face, plastic surgery and informatics are employed, representing cyber forensics with challenges. The classic facial recognition techniques suffer from major drawbacks when face masks are widely used. As a result, new techniques are now being tried and tested to reconstruct a face from a collection of masked facial images. To determine the identification accuracy and other parameters/metrics, these faces are compared to real-world images of the same subject.\u003c/p\u003e \u003cp\u003eOur research focuses on the task of post-mask face reconstruction, addressing the pressing need for precise and reliable techniques. We evaluate the effectiveness of three key algorithms: Edge Connect, Gated Convolution, and Hierarchical Variational Vector Quantized Autoencoders (HVQVAE). 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