{"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-expression-recognition-using-residual","title":"Facial Expression Recognition using Residual Masking Network","arxiv_id":null,"date":"2021-05-05","proceeding":"International Conference on Pattern Recognition 2021 5","authors":["Luan Pham","The Huynh Vu","Tuan Anh Tran"],"abstract":"Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking Idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets.","url_abs":"https://ieeexplore.ieee.org/document/9411919","url_pdf":"https://ieeexplore.ieee.org/document/9411919","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-expression-recognition-using-residual","repo_url":"https://github.com/phamquiluan/ResidualMaskingNetwork","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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":"rmn","method_name":"RMN"}],"datasets_introduced":[],"methods_introduced":[{"slug":"rmn","name":"RMN","full_name":"Residual Masking Network"}],"results":[{"leaderboard":"/sota/facial-expression-recognition-on-fer2013","task":"Facial Expression Recognition (FER)","dataset":"FER2013","model":"Ensemble ResMaskingNet with 6 other CNNs","rank_in_archive_order":4,"of":17,"metrics":{"Accuracy":"76.82"},"uses_additional_data":true},{"leaderboard":"/sota/facial-expression-recognition-on-fer2013","task":"Facial Expression Recognition (FER)","dataset":"FER2013","model":"Residual Masking Network","rank_in_archive_order":10,"of":17,"metrics":{"Accuracy":"74.14"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}