{"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/former-dfer-dynamic-facial-expression-1","title":"Former-DFER: Dynamic Facial Expression Recognition Transformer","arxiv_id":null,"date":"2021-10-17","proceeding":"ACM International Conference on Multimedia 2021 10","authors":["Zengqun Zhao","Qingshan Liu"],"abstract":"This paper proposes a dynamic facial expression recognition transformer (Former-DFER) for the in-the-wild scenario. Specifically, the proposed Former-DFER mainly consists of a convolutional spatial transformer (CS-Former) and a temporal transformer (T-Former). The CS-Former consists of five convolution blocks and N spatial encoders, which is designed to guide the network to learn occlusion and pose-robust facial features from the spatial perspective. And the temporal transformer consists of M temporal encoders, which is designed to allow the network to learn contextual facial features from the temporal perspective. The heatmaps of the leaned facial features demonstrate that the proposed Former-DFER is capable of handling the issues such as occlusion, non-frontal pose, and head motion. And the visualization of the feature distribution shows that the proposed method can learn more discriminative facial features. Moreover, our Former-DFER also achieves state-of-the-art results on the DFEW and AFEW benchmarks.","url_abs":"https://dl.acm.org/doi/10.1145/3474085.3475292","url_pdf":"https://drive.google.com/file/d/12vyWD4mJ9HCkLyBctoPcvUbOU36Ptgc8/view","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":"former-dfer-dynamic-facial-expression-1","repo_url":"https://github.com/zengqunzhao/Former-DFER","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dynamic-facial-expression-recognition","task_name":"Dynamic Facial Expression Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}