CNN in Neural Networks for Image-based Face Emotion Identification on Recognition Datasets

CNN in Neural Networks for Image-based Face Emotion Identification on Recognition Datasets | 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 CNN in Neural Networks for Image-based Face Emotion Identification on Recognition Datasets Monalisa Hati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6392183/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 Because facial expressions can vary greatly, it can be challenging to identify emotions from face photographs. Prior studies on the use of deep learning models for facial image emotion classification have been conducted on a variety of datasets with a restricted range of expressions. The Recognition dataset, which contains ten target emotions—amusement, awe, enthusiasm, liking, surprise, anger, contempt, fear, sorrow, and neutral—is used in this work to extend the application of deep learning for facial emotion recognition (FER). To transform video data into photos and enhance the data, a number of data preparation steps were taken. This paper suggests two methods for creating Convolutional Neural Network (CNN) models: transfer learning (fine-tuning) with pre-trained Inception V3 and Mobile Net V2 models and starting from scratch using the Taguchi technique to determine. In order to establish a reliable combination of hyperparameter settings, this study suggests two methods for developing Convolutional Neural Network (CNN) models: transfer learning (fine-tuned) with pre-trained models of Inception V3 and Mobile Net V2, and building from scratch using the Taguchi technique. With an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data, the suggested model showed good performance across a number of experimental procedures. CNN Mobile Net Inception V3 FER F1-Score 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. 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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-6392183","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439849178,"identity":"72652577-86b2-44c4-b207-85be76f2304a","order_by":0,"name":"Monalisa Hati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYLCCBAYbORB94AHxWhLSjMFaEkiw5lBiA8Q6IoB8/+GnGx7+OJA+P+zwQ6AtdnK6DQS0GNxIM7uRkHAnd+PtNAOglmRjswOEtEgwgLQ8y904OwGk5UDiNkJa5PuPfwNqOZxuODv9A3FaGA7kgGw5nCAvnUOkLQY3cspuJKSlGW6Qzik4kGBAhF+ADtt284eNjbz87PTNHz5U2MkR1IKwDqzSgFjlYOsaSFE9CkbBKBgFIwoAADrMTHHGmKSBAAAAAElFTkSuQmCC","orcid":"","institution":"AMITY UNIVERSITY","correspondingAuthor":true,"prefix":"","firstName":"Monalisa","middleName":"","lastName":"Hati","suffix":""}],"badges":[],"createdAt":"2025-04-07 08:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6392183/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6392183/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80814263,"identity":"e0cd0fb6-3566-4bfe-a67a-d1828f39798f","added_by":"auto","created_at":"2025-04-17 10:47:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":786161,"visible":true,"origin":"","legend":"","description":"","filename":"1RESEARCHPAPERMONALISAHATI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6392183/v1_covered_98252c08-72ed-4e1f-803d-aea6e098e1fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CNN in Neural Networks for Image-based Face Emotion Identification on Recognition Datasets","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":"CNN, Mobile Net, Inception V3, FER, F1-Score","lastPublishedDoi":"10.21203/rs.3.rs-6392183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6392183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBecause facial expressions can vary greatly, it can be challenging to identify emotions from face photographs. Prior studies on the use of deep learning models for facial image emotion classification have been conducted on a variety of datasets with a restricted range of expressions. The Recognition dataset, which contains ten target emotions\u0026mdash;amusement, awe, enthusiasm, liking, surprise, anger, contempt, fear, sorrow, and neutral\u0026mdash;is used in this work to extend the application of deep learning for facial emotion recognition (FER). To transform video data into photos and enhance the data, a number of data preparation steps were taken. This paper suggests two methods for creating Convolutional Neural Network (CNN) models: transfer learning (fine-tuning) with pre-trained Inception V3 and Mobile Net V2 models and starting from scratch using the Taguchi technique to determine. 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