GiMeFive: Towards Interpretable Facial Emotion Classification

GiMeFive: Towards Interpretable Facial Emotion Classification | 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 Short Report GiMeFive: Towards Interpretable Facial Emotion Classification Jiawen Wang, Leah Kawka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3997302/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 Deep convolutional neural networks have been shown to successfully recognize facial emotions for the past years in the realm of computer vision. However, the existing detection approaches are not always reliable or explainable, we here propose our model GiMeFive with interpretations, i.e., via layer activations and gradient-weighted class activation mapping. We compare against the state-of-the-art methods to classify the six facial emotions. Empirical results show that our model outperforms the previous methods in terms of accuracy on two Facial Emotion Recognition (FER) benchmarks and our aggregated FER GiMeFive. Furthermore, we explain our work in real-world image and video examples, as well as real-time live streams. Our code and supplementary material are available at https: //github.com/werywjw/SEP-CVDL . facial emotion recognition interpretable emotion classification explainable AI Full Text Additional Declarations The authors declare no competing interests. This research is conducted in accordance with the ethical guidelines by the agreement on use of video data. The studies involving human participants were reviewed and approved by the IRB committee of Carnegie Mellon University. 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-3997302","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":275439560,"identity":"66b6d839-9bf0-4682-a1c7-45d3a45e1efa","order_by":0,"name":"Jiawen Wang","email":"data:image/png;base64,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","orcid":"","institution":"Institute of Informatics","correspondingAuthor":true,"prefix":"","firstName":"Jiawen","middleName":"","lastName":"Wang","suffix":""},{"id":275439561,"identity":"ccd55a91-998d-46f2-9dad-4cbcfe45b2a1","order_by":1,"name":"Leah Kawka","email":"","orcid":"","institution":"Institute of Informatics","correspondingAuthor":false,"prefix":"","firstName":"Leah","middleName":"","lastName":"Kawka","suffix":""}],"badges":[],"createdAt":"2024-02-28 17:02:11","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-3997302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3997302/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51984690,"identity":"01bf9c78-9a30-45c9-aaa3-3065e3ca8adb","added_by":"auto","created_at":"2024-03-05 01:36:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2357950,"visible":true,"origin":"","legend":"","description":"","filename":"main.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3997302/v1_covered_3c4a610e-0f89-41b3-b5aa-bb7395f5ff83.pdf"}],"financialInterests":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eThis research is conducted in accordance with the ethical guidelines by the agreement on use of video data. 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