Imperial College London

STEFANOS ZAFEIRIOU, PhD

Faculty of EngineeringDepartment of Computing

Reader in Machine Learning and Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Cheng:2018:10.1109/CVPR.2018.00537,
author = {Cheng, S and Kotsia, I and Pantic, M and Zafeiriou, S},
doi = {10.1109/CVPR.2018.00537},
pages = {5117--5126},
publisher = {IEEE},
title = {4DFAB: A large scale 4D database for facial expression analysis and biometric applications},
url = {http://dx.doi.org/10.1109/CVPR.2018.00537},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - 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.
AU - Cheng,S
AU - Kotsia,I
AU - Pantic,M
AU - Zafeiriou,S
DO - 10.1109/CVPR.2018.00537
EP - 5126
PB - IEEE
PY - 2018///
SN - 1063-6919
SP - 5117
TI - 4DFAB: A large scale 4D database for facial expression analysis and biometric applications
UR - http://dx.doi.org/10.1109/CVPR.2018.00537
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000457843605028&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/71007
ER -