4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications
Abstract
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 contain recordings of 180 subjects captured in four different sessions spanned 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 in various applications. The database will be made publicly available for research purposes.
Cite
Text
Cheng et al. "4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00537Markdown
[Cheng et al. "4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/cheng2018cvpr-4dfab/) doi:10.1109/CVPR.2018.00537BibTeX
@inproceedings{cheng2018cvpr-4dfab,
title = {{4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications}},
author = {Cheng, Shiyang and Kotsia, Irene and Pantic, Maja and Zafeiriou, Stefanos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00537},
url = {https://mlanthology.org/cvpr/2018/cheng2018cvpr-4dfab/}
}