A Robust Lightweight Compound Emotion Recognition Approach Using Depthwise Separable CNN

A Robust Lightweight Compound Emotion Recognition Approach Using Depthwise Separable CNN | 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 A Robust Lightweight Compound Emotion Recognition Approach Using Depthwise Separable CNN Sana Ullah, Yuanlun Xie, Jie Ou, Zhaokun Wang, Wenhong Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4354821/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 With the innovation in computer vision, facial expression recognition (FER) is a dynamic research domain considering extensive practical applications in various domains together with health, education, safety, law enforcement, Banking, marketing, and many more. The researchers have conducted tremendous research work on basic facial expressions recognition, but less on compound emotions recognition, which have complex features due to the combination of basic emotions. Different deep learning models have been used for compound emotions recognition; however, these deep learning models are computationally expensive due to large parameters and training time. To overcome this problem, we have proposed a robust lightweight approach using depthwise separable convolution (DSC), and residual connections. The proposed model outperformed the state-of-the-art (SOTA) models with an achieved accuracy of 70.4% on the RAFDB dataset, and 67.2% on the CFEE dataset. The proposed model improved the accuracy performance of compound emotion recognition by 1.9% on the RAFDB dataset, and 9.8% on the CFEE dataset from the SOTA models. The proposed approach reduced model parameters, and memory consumption compared with the deep learning model of standard convolution. Compound Emotion Emotion’s recognition Depthwise Separable Convolution Deep Learning Lightweight Model 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-4354821","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299534643,"identity":"affa3518-fef4-41d9-aa1f-8d18d281163b","order_by":0,"name":"Sana Ullah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBADHn4QmVBAghYZyQaQFgMStNgYHABRxGiRbz978DNPxTYe4/OrEz88MGCQ5xc7gF+LwZm8ZGmeM7d5zG683SwBdJjhzNkJBLQw5BhI87aBtJzdANKSYHCbgBb5/jfGv0FajGec3fyDKC0MN3LMwLYY8PduI84WgxtvzCznAP0icYN3m0WCgQRhv8j35xjfeFNx256//+zmmz8qbOT5pQk5DA4kwColiFUOAvwHSFE9CkbBKBgFIwkAABD9QmmToKbjAAAAAElFTkSuQmCC","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Sana","middleName":"","lastName":"Ullah","suffix":""},{"id":299534645,"identity":"45fe4892-40ab-4084-944d-80dbfcf1b977","order_by":1,"name":"Yuanlun Xie","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yuanlun","middleName":"","lastName":"Xie","suffix":""},{"id":299534647,"identity":"a91a0297-2cde-420c-b572-6a6b11aee66e","order_by":2,"name":"Jie Ou","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Ou","suffix":""},{"id":299534649,"identity":"a2d35b98-38b8-4ac3-a1c6-e104f41a00ab","order_by":3,"name":"Zhaokun Wang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Zhaokun","middleName":"","lastName":"Wang","suffix":""},{"id":299534652,"identity":"6094f2db-3bdc-4406-8bdb-3d1a084ead9d","order_by":4,"name":"Wenhong Tian","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Wenhong","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2024-05-01 14:41:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4354821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4354821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58770019,"identity":"283f1ee8-040b-46d7-aec4-1d4aceeb177a","added_by":"auto","created_at":"2024-06-21 00:23:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":679614,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4354821/v1_covered_710f522c-8223-48da-ae4d-3b66a95f178f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Robust Lightweight Compound Emotion Recognition Approach Using Depthwise Separable CNN","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":"Compound Emotion, Emotion’s recognition, Depthwise Separable Convolution, Deep Learning, Lightweight Model","lastPublishedDoi":"10.21203/rs.3.rs-4354821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4354821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the innovation in computer vision, facial expression recognition (FER) is a dynamic research domain considering extensive practical applications in various domains together with health, education, safety, law enforcement, Banking, marketing, and many more. 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