Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment

Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment | 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 Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment Kangsheng Wang, Chengwei Ye, Huanzhen Zhang, Linuo Xu, Shuyan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6455809/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 Predicting personality traits automatically has emerged as a challenging problem in computer vision. This paper introduces an innovative multimodal feature learning framework for personality analysis in short video clips. For visual processing, we construct a facial graph and design a Geo-based two-stream network incorporating an attention mechanism, leveraging both Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to capture static facial expressions. Additionally, ResNet18 and VGGFace networks are employed to extract global scene and facial appearance features at the frame level. To capture dynamic temporal information, we integrate a BiGRU with a temporal attention module for extracting salient frame representations. To further enhance the model’s robustness, we incorporate the VGGish CNN for audio-based features and XLM-Roberta for text-based features. Finally, a multimodal channel attention mechanism is introduced to integrate different modalities, and a Multi-Layer Perceptron (MLP) regression model is utilized to predict personality traits. Experimental results confirm that our proposed framework surpasses existing state-of-the-art approaches in performance. Personality Prediction Facial Graph Graph Convolutional Network(GCN) Convolutional Neural Network(CNN) Attention Mechanism Geometric and Appearance features Full Text Additional Declarations The authors declare no competing interests. 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-6455809","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443341467,"identity":"139504ee-4c5e-47df-b97c-edf6f825e18a","order_by":0,"name":"Kangsheng Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kangsheng","middleName":"","lastName":"Wang","suffix":""},{"id":443341468,"identity":"232fe885-fe9e-4c99-ad7f-f2d1ee858381","order_by":1,"name":"Chengwei Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACCQjFw8DA2PjgA5DFxk68FuZmwxkgLcxEagEC9jZpHhBNSAv/7OZjD7+22cgYHG9sNrb5tU2ej5mB8cPHHDyW3DmWbizblsZjcOZg4+PcvtuGbcwMzJIzt+HWYiCRYyYtue0wj8GNxGbj3J7bjEAtbMy8eLXkfwNq+c9jcP9hm7Rlz217IrTksEl+3HYAaAtjmzTDj9uJBLVI3Egzk2b8l8wjeSax2bC34XZyGzNjM16/8M9Ifib544ydPd/x4w8f/Phz23Z+e/PBDx/xaAEBZlB0KBwAEoxtID5jA371ICU/gIQ8WN0fgopHwSgYBaNgBAIAMaBSbQSp9+QAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Chengwei","middleName":"","lastName":"Ye","suffix":""},{"id":443341469,"identity":"d5f5c071-c830-4c13-8a12-58d277ddaab1","order_by":2,"name":"Huanzhen Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Huanzhen","middleName":"","lastName":"Zhang","suffix":""},{"id":443341470,"identity":"727d5e01-f814-411a-8b3b-5b34b4c94f45","order_by":3,"name":"Linuo Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Linuo","middleName":"","lastName":"Xu","suffix":""},{"id":443341471,"identity":"f8d2da52-d173-430f-8013-6374d1cf29dd","order_by":4,"name":"Shuyan Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shuyan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-04-15 14:31:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6455809/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6455809/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80722182,"identity":"98df5958-596a-47aa-86af-4e39b7986a1e","added_by":"auto","created_at":"2025-04-16 11:11:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":596727,"visible":true,"origin":"","legend":"","description":"","filename":"GraphDrivenMultimodalFeatureLearningFrameworkforApparentPersonalityAssessment3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6455809/v1_covered_362beccd-0f70-450a-ba27-d26b8aba7ea1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGraph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Science and Techonology Beijing","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Personality Prediction, Facial Graph, Graph Convolutional Network(GCN), Convolutional Neural Network(CNN), Attention Mechanism , Geometric and Appearance features","lastPublishedDoi":"10.21203/rs.3.rs-6455809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6455809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting personality traits automatically has emerged as a challenging problem in computer vision. 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