{"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/dexpression-deep-convolutional-neural-network","title":"DeXpression: Deep Convolutional Neural Network for Expression Recognition","arxiv_id":"1509.05371","date":"2015-09-17","proceeding":null,"authors":["Peter Burkert","Felix Trier","Muhammad Zeshan Afzal","Andreas Dengel","Marcus Liwicki"],"abstract":"We propose a convolutional neural network (CNN) architecture for facial\nexpression recognition. The proposed architecture is independent of any\nhand-crafted feature extraction and performs better than the earlier proposed\nconvolutional neural network based approaches. We visualize the automatically\nextracted features which have been learned by the network in order to provide a\nbetter understanding. The standard datasets, i.e. Extended Cohn-Kanade (CKP)\nand MMI Facial Expression Databse are used for the quantitative evaluation. On\nthe CKP set the current state of the art approach, using CNNs, achieves an\naccuracy of 99.2%. For the MMI dataset, currently the best accuracy for emotion\nrecognition is 93.33%. The proposed architecture achieves 99.6% for CKP and\n98.63% for MMI, therefore performing better than the state of the art using\nCNNs. Automatic facial expression recognition has a broad spectrum of\napplications such as human-computer interaction and safety systems. This is due\nto the fact that non-verbal cues are important forms of communication and play\na pivotal role in interpersonal communication. The performance of the proposed\narchitecture endorses the efficacy and reliable usage of the proposed work for\nreal world applications.","url_abs":"http://arxiv.org/abs/1509.05371v2","url_pdf":"http://arxiv.org/pdf/1509.05371v2.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":"dexpression-deep-convolutional-neural-network","repo_url":"https://github.com/MaxLikesMath/DeepLearningImplementations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"dexpression-deep-convolutional-neural-network","repo_url":"https://github.com/ckapoor7/DeXpression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null},{"paper_slug":"dexpression-deep-convolutional-neural-network","repo_url":"https://github.com/rdgozum/dexpression-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"}],"methods":[{"method_slug":"adam","method_name":"Adam"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-expression-recognition-on-mmi","task":"Facial Expression Recognition (FER)","dataset":"MMI","model":"DeXpression","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"98.63"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}