{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/facial-emotion-recognition-using-transfer","title":"Facial Emotion Recognition Using Transfer Learning in the Deep CNN","arxiv_id":null,"date":"2021-04-27","proceeding":"Electronics 2021 4","authors":["M. A. H. Akhand","Shuvendu Roy","Nazmul Siddique","Md Abdus Samad Kamal","Tetsuya Shimamura"],"abstract":"Human facial emotion recognition (FER) has attracted the attention of the research community for its promising applications. Mapping different facial expressions to the respective emotional states are the main task in FER. The classical FER consists of two major steps: feature extraction and emotion recognition. Currently, the Deep Neural Networks, especially the Convolutional Neural Network (CNN), is widely used in FER by virtue of its inherent feature extraction mechanism from images. Several works have been reported on CNN with only a few layers to resolve FER problems. However, standard shallow CNNs with straightforward learning schemes have limited\r\nfeature extraction capability to capture emotion information from high-resolution images.","url_abs":"https://www.mdpi.com/2079-9292/10/9/1036","url_pdf":"https://www.mdpi.com/2079-9292/10/9/1036/pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"facial-emotion-recognition-using-transfer","repo_url":"https://github.com/ShuvenduRoy/FER_TL_PipelineTraining","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"facial-emotion-recognition-using-transfer","repo_url":"https://github.com/EverLookNeverSee/fer_tl_dcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"facial-emotion-recognition","task_name":"Facial Emotion Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-emotion-recognition-on-jaffe","task":"Facial Emotion Recognition","dataset":"JAFFE","model":"TL","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"99.52"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-jaffe","task":"Facial Expression Recognition (FER)","dataset":"JAFFE","model":"TL","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"99.52"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}