Efficient Face Detection and Recognition with PCA and Eigenfaces: A Comprehensive Analysis

Efficient Face Detection and Recognition with PCA and Eigenfaces: A Comprehensive Analysis | 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 Efficient Face Detection and Recognition with PCA and Eigenfaces: A Comprehensive Analysis Mohammad Alamgir Hossain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6537649/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 Face detection and recognition are critical tasks in computer vision with applications in security systems, biometric authentication, and human-computer interaction. This paper presents a comprehensive study leveraging Principal Component Analysis (PCA) and Eigenfaces for efficient dimensionality reduction and compact, discrimina-tive facial feature representation. The study introduces a robust pipeline integrating preprocessing, feature extraction, and efficient training. Using the CelebA dataset for training and the LFW dataset for evaluation, the system addresses real-world challenges, including variations in lighting, expressions, and poses. The performance is analyzed across configurations, exploring the trade-off between dimensionality reduction and recognition accuracy. Experimental results demonstrate that the PCA-based approach achieves high recognition accuracy (95% on controlled datasets) while maintaining computational efficiency, making it suitable for resource-constrained environments. The findings highlight the system’s robustness, scalability, and practical applicability in both constrained and real-time scenarios. This work concludes with an analysis of strengths and limitations and offers recommendations for integrating non-linear techniques and advanced learning models to further enhance scalability, accuracy, and real-world performance. PCA Eigenfaces Face Detection Face Recognition Machine Learning Computer Vision 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. 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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-6537649","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451715128,"identity":"c3532819-3934-427f-8e76-3262845c6ef9","order_by":0,"name":"Mohammad Alamgir Hossain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACdhBhwCDHwMDYeAAmKIFXCzNEizFQS8MBmGoitDAwJDYACeK08DczP934peBe+tr2ww0HGNvq6vgbmA/e5mGwy8OlReIwm9ltGYPi3G1nEkFaDktIHGBLtuZhSC7Gac1hBrPbEgYJudsOgLUckDBg4DGT5mE4AHYqNiB/mP0bSEu62fmHYIcBtfB/w6vF4DCP2c0PBgkJZjfAtjCDbGHDq8XwME/ZbQaDBMNtN4C2JJw7LDnjMJux5RyDZJxa5I63b7v540+CvNn59IcPPpTV8fO3Nz+88abCDqcWEGDmgbESwFywg/GoBwLGH/jlR8EoGAWjYKQDALUaV+GdCu72AAAAAElFTkSuQmCC","orcid":"","institution":"University of Science and Technology of China (USTC)","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Alamgir","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2025-04-27 03:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6537649/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6537649/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83726831,"identity":"fb8ed623-51ec-404a-ab2e-b02f63aac4d7","added_by":"auto","created_at":"2025-06-01 09:46:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457179,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6537649/v1_covered_d131fdae-ac47-4640-ba09-828018cf9bfc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Face Detection and Recognition with PCA and Eigenfaces: A Comprehensive Analysis","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":"PCA, Eigenfaces, Face Detection, Face Recognition, Machine Learning, Computer Vision","lastPublishedDoi":"10.21203/rs.3.rs-6537649/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6537649/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Face detection and recognition are critical tasks in computer vision with applications in security systems, biometric authentication, and human-computer interaction. 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