{"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/a-dual-direction-attention-mixed-feature","title":"A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition","arxiv_id":null,"date":"2023-08-25","proceeding":"journal 2023 8","authors":["Saining Zhang","Yuhang Zhang","Ye Zhang","YuFei Wang","Zhigang Song"],"abstract":"In recent years, facial expression recognition (FER) has garnered significant attention within the realm of computer vision research. This paper presents an innovative network called the Dual-Direction Attention Mixed Feature Network (DDAMFN) specifically designed for FER, boasting both robustness and lightweight characteristics. The network architecture comprises two primary components: the Mixed Feature Network (MFN) serving as the backbone, and the Dual-Direction Attention Network (DDAN) functioning as the head. To enhance the network’s capability in the MFN, resilient features are extracted by utilizing mixed-size kernels. Additionally, a new Dual-Direction Attention (DDA) head that generates attention maps in two orientations is proposed, enabling the model to capture long-range dependencies effectively. To further improve the accuracy, a novel attention loss mechanism for the DDAN is introduced with different heads focusing on distinct areas of the input. Experimental evaluations on several widely used public datasets, including AffectNet, RAF-DB, and FERPlus, demonstrate the superiority of the DDAMFN compared to other existing models, which establishes that the DDAMFN as the state-of-the-art model in the field of FER.","url_abs":"https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C","url_pdf":"https://doi.org/10.3390/electronics12173595","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":"a-dual-direction-attention-mixed-feature","repo_url":"https://github.com/simon20010923/DDAMFN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-expression-recognition-on-affectnet","task":"Facial Expression Recognition (FER)","dataset":"AffectNet","model":"DDAMFN++","rank_in_archive_order":2,"of":50,"metrics":{"Accuracy (7 emotion)":"67.36","Accuracy (8 emotion)":"65.04"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-affectnet","task":"Facial Expression Recognition (FER)","dataset":"AffectNet","model":"DDAMFN","rank_in_archive_order":6,"of":50,"metrics":{"Accuracy (7 emotion)":"67.03","Accuracy (8 emotion)":"64.25"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-fer-1","task":"Facial Expression Recognition (FER)","dataset":"FER+","model":"DDAMFN","rank_in_archive_order":5,"of":14,"metrics":{"Accuracy":"90.74"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-raf-db","task":"Facial Expression Recognition (FER)","dataset":"RAF-DB","model":"DDAMFN++","rank_in_archive_order":8,"of":35,"metrics":{"Overall Accuracy":"92.34"},"uses_additional_data":true},{"leaderboard":"/sota/facial-expression-recognition-on-raf-db","task":"Facial Expression Recognition (FER)","dataset":"RAF-DB","model":"DDAMFN","rank_in_archive_order":12,"of":35,"metrics":{"Overall Accuracy":"91.35"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}