Micro-expression Action Unit Recognition Based on Dynamic Image and Spatial Pyramid

Micro-expression Action Unit Recognition Based on Dynamic Image and Spatial Pyramid | 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 Micro-expression Action Unit Recognition Based on Dynamic Image and Spatial Pyramid Guanqun Zhou, Shusen Yuan, Hongbo Xing, Youjun Jiang, Pinyong Geng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2449787/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Most of the existing research focuses on the recognition of micro-expressions, and few studies how to recognize the action units of micro-expressions. This is due to the low intensity of the facial action unit, which is not easily to be recognized. To solve this problem, we proposed a micro-expression action unit recognition algorithm based on dynamic image and spatial pyramids. First, the video is passed through the dynamic image generation module to generate a dynamic image and extract the motion information contained in all frames. Then, given the subtle movement properties of micro-expressions, different levels of semantic features are obtained through spatial pyramids. It is also known that micro-expressions appear in the small range and are concentrated in local area of the face, so the regional feature network and attention mechanism are used for the image features of each layer. Finally, due to the weak correlation between each action unit, our models are trained separately. Experiments on CASME and CAS(ME)2 datasets verify that the proposed algorithm has shown better action unit recognition performance compared with other advanced methods. Micro-expression Action unit recognition Dynamic image Spatial pyramids Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 25 Apr, 2023 Reviews received at journal 14 Apr, 2023 Reviewers agreed at journal 10 Apr, 2023 Reviewers agreed at journal 10 Apr, 2023 Reviewers invited by journal 10 Apr, 2023 Editor assigned by journal 06 Jan, 2023 Submission checks completed at journal 06 Jan, 2023 First submitted to journal 06 Jan, 2023 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. 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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-2449787","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":165695196,"identity":"ac975c5d-3283-429e-8e01-644d56e0534b","order_by":0,"name":"Guanqun Zhou","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guanqun","middleName":"","lastName":"Zhou","suffix":""},{"id":165695197,"identity":"57216223-977c-495a-bf6e-a97633ae7772","order_by":1,"name":"Shusen Yuan","email":"","orcid":"","institution":"Shandong 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