Partitioned Region-focused and Graph Convolution Hybrid Network for Macro- and Micro-expression Spotting

Partitioned Region-focused and Graph Convolution Hybrid Network for Macro- and Micro-expression Spotting | 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 Partitioned Region-focused and Graph Convolution Hybrid Network for Macro- and Micro-expression Spotting Hang Yi, Yan Dou, Nannan Yan, Xiaoyan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6539700/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 Recently, the analysis of facial macro- and micro-expressions has attracted the attention of researchers. However, spotting micro-expressions in long video sequences is a significant challenge due to their subtle and transient nature, which makes it difficult for current methods to pinpoint the precise time intervals of these fleeting expressions accurately. To address this issue, we propose the hybrid lightweight, partitioned region-focused and graph convolution network \(\text{(PRF-GCN)}\) . The proposed model incorporates a partitioned region-focused convolutional approach that aligns feature extraction with regions of interest based on human attention mechanisms, to capture fine-grained motion cues effectively. The extracted features are then transformed into graph structures to facilitate relational modeling and reasoning through GCNs. By predicting the probability that each frame belongs to a micro-expression interval, the proposed framework offers enhanced temporal localization capabilities. Experimental results obtained on the \(\text{CAS(ME)}^2\) and \(\text{SAMM-LV}\) datasets demonstrate strong performance, with F1-scores of 0.2124 and 0.1733, respectively. In addition, extensive ablation studies further confirm the effectiveness of the proposed \(\text{PRF-GCN}\) . macro- and micro-expression spotting graph convolutional network (GCN) partitioned convolution attention mechanism long videos 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-6539700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452831692,"identity":"487a3ff1-a20c-4acb-9da4-6fe6c44e3a44","order_by":0,"name":"Hang Yi","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Yi","suffix":""},{"id":452831693,"identity":"1a04b590-6858-4cdc-95e7-fc8ba42d2d5a","order_by":1,"name":"Yan Dou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDCCA2DSBsLhIUFLGulaDpOghe94j5nEzx3nE+fPSGB88LaNQd6ckBbJM2fMJHvP3E7ccCOB2XBuG4PhzgYCWgxu5JhJ8LYBtUgksEnztjEkGBwgQovk37ZzIIex/yZaC9DwA4kNNxLYmInSInnmWLG1bFuy8YYzD5sl55yTMNxASAvf8eaNN9+22cnOb08++OFNmY08QVuAgEUCSDg2MDA2AGkJwuqBgPkDkLAnSukoGAWjYBSMTAAAjJhDkew3FfEAAAAASUVORK5CYII=","orcid":"","institution":"Yanshan University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Dou","suffix":""},{"id":452831694,"identity":"bac775ac-9668-47bf-9421-6407f998854e","order_by":2,"name":"Nannan Yan","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"Nannan","middleName":"","lastName":"Yan","suffix":""},{"id":452831695,"identity":"37cb5348-f091-4a3a-9b76-5078446313de","order_by":3,"name":"Xiaoyan Wang","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-04-27 10:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6539700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6539700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87452049,"identity":"a1a0f304-b0e6-4572-85df-5e8959066c14","added_by":"auto","created_at":"2025-07-24 03:09:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9950430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6539700/v1_covered_5534a9fc-7227-4f7e-9c34-e1145cfc7de3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Partitioned Region-focused and Graph Convolution Hybrid Network for Macro- and Micro-expression Spotting","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"macro- and micro-expression spotting, graph convolutional network (GCN), partitioned convolution, attention mechanism, long videos","lastPublishedDoi":"10.21203/rs.3.rs-6539700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6539700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecently, the analysis of facial macro- and micro-expressions has attracted the attention of researchers. However, spotting micro-expressions in long video sequences is a significant challenge due to their subtle and transient nature, which makes it difficult for current methods to pinpoint the precise time intervals of these fleeting expressions accurately. To address this issue, we propose the hybrid lightweight, partitioned region-focused and graph convolution network \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{(PRF-GCN)}\\)\u003c/span\u003e\u003c/span\u003e. The proposed model incorporates a partitioned region-focused convolutional approach that aligns feature extraction with regions of interest based on human attention mechanisms, to capture fine-grained motion cues effectively. The extracted features are then transformed into graph structures to facilitate relational modeling and reasoning through GCNs. By predicting the probability that each frame belongs to a micro-expression interval, the proposed framework offers enhanced temporal localization capabilities. Experimental results obtained on the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{CAS(ME)}^2\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{SAMM-LV}\\)\u003c/span\u003e\u003c/span\u003e datasets demonstrate strong performance, with F1-scores of 0.2124 and 0.1733, respectively. 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