{"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/4dfab-a-large-scale-4d-facial-expression","title":"4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications","arxiv_id":"1712.01443","date":"2017-12-05","proceeding":null,"authors":["Shiyang Cheng","Irene Kotsia","Maja Pantic","Stefanos Zafeiriou"],"abstract":"The progress we are currently witnessing in many computer vision\napplications, including automatic face analysis, would not be made possible\nwithout tremendous efforts in collecting and annotating large scale visual\ndatabases. To this end, we propose 4DFAB, a new large scale database of dynamic\nhigh-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings\nof 180 subjects captured in four different sessions spanning over a five-year\nperiod. It contains 4D videos of subjects displaying both spontaneous and posed\nfacial behaviours. The database can be used for both face and facial expression\nrecognition, as well as behavioural biometrics. It can also be used to learn\nvery powerful blendshapes for parametrising facial behaviour. In this paper, we\nconduct several experiments and demonstrate the usefulness of the database for\nvarious applications. The database will be made publicly available for research\npurposes.","url_abs":"http://arxiv.org/abs/1712.01443v2","url_pdf":"http://arxiv.org/pdf/1712.01443v2.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":"4dfab-a-large-scale-4d-facial-expression","repo_url":"https://github.com/sw-gong/spiralnet_plus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.01443","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}