S. Zafeiriou, G. G. Chrysos, A. Roussos, E. Ververas, J. Deng and G. Trigeorgis, "The 3D Menpo Facial Landmark Tracking Challenge," 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, 2017, pp. 2503-2511. doi: 10.1109/ICCVW.2017.16
The 3D menpo facial landmark tracking challenge
|Author:||Zafeiriou, Stefanos1,2; Chrysos, Grigorios G.1; Roussos, Anastasios1,3;|
1Department of Computing, Imperial College London, UK
2Center for Machine Vision and Signal Analysis, University of Oulu, Finland
3Department of Computer Science, University of Exeter, UK
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019042613367
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-04-26
Recently, deformable face alignment is synonymous to the task of locating a set of 2D sparse landmarks in intensity images. Currently, discriminatively trained Deep Convolutional Neural Networks (DCNNs) are the state-of-the-art in the task of face alignment. DCNNs exploit large amount of high quality annotations that emerged the last few years. Nevertheless, the provided 2D annotations rarely capture the 3D structure of the face (this is especially evident in the facial boundary). That is, the annotations neither provide an estimate of the depth nor correspond to the 2D projections of the 3D facial structure. This paper summarises our efforts to develop (a) a very large database suitable to be used to train 3D face alignment algorithms in images captured “in-the-wild” and (b) to train and evaluate new methods for 3D face landmark tracking. Finally, we report the results of the first challenge in 3D face tracking “in-the-wild”.
Symposium on Photonics and Optoelectronics
|Pages:||2503 - 2511|
2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
IEEE International Conference on Computer Vision Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
The work of S. Zafeiriou and A. Roussos has been partially funded by the EPSRC Project EP/N007743/1.
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