The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases. To this end, we propose 4DFAB, a new large scale database of dynamic high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour. In this paper, we conduct several experiments and demonstrate the usefulness of the database for various applications. The database will be made publicly available for research purposes.
|Title of host publication||30th IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2017|
|Subtitle of host publication||21-26 July 2016, Honolulu, Hawaii : proceedings|
|Editors||Rama Chellappa, Zhengyou Zhang, Anthony Hoogs|
|Number of pages||12|
|Publication status||Published - 5 Dec 2017|
|Event||30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Hawaii Convention Center, Honolulu, United States|
Duration: 21 Jul 2017 → 26 Jul 2017
Conference number: 30
|Conference||30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017|
|Period||21/07/17 → 26/07/17|
Cheng, S., Kotsia, I., Pantic, M., & Zafeiriou, S. (2017). 4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications. In R. Chellappa, Z. Zhang, & A. Hoogs (Eds.), 30th IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2017: 21-26 July 2016, Honolulu, Hawaii : proceedings IEEE.