E. Antonakos, P. Snape, G. Trigeorgis and S. Zafeiriou, "Adaptive cascaded regression," 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, 2016, pp. 1649-1653. doi: 10.1109/ICIP.2016.7532638
Adaptive cascaded regression
|Author:||Antonakos, Epameinondas1,2; Snape, Patrick1,2; Trigeorgis, George1;|
1Department of Computing, Imperial College London, U.K.
2Center for Machine Vision and Signal Analysis, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201902286522
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-02-28
The two predominant families of deformable models for the task of face alignment are: (i) discriminative cascaded regression models, and (ii) generative models optimised with Gauss-Newton. Although these approaches have been found to work well in practise, they each suffer from convergence issues. Cascaded regression has no theoretical guarantee of convergence to a local minimum and thus may fail to recover the fine details of the object. Gauss-Newton optimisation is not robust to initialisations that are far from the optimal solution. In this paper, we propose the first, to the best of our knowledge, attempt to combine the best of these two worlds under a unified model and report state-of-the-art performance on the most recent facial benchmark challenge.
IEEE International Conference on Image Processing
|Pages:||1649 - 1653|
2016 IEEE International Conference on Image Processing (ICIP)
IEEE international conference on image processing
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
The work of E. Antonakos and P. Snape was funded by the European Community Horizon 2020 [H2020/2014-2020] under grant agreement no. 688520 (TeSLA). G. Trigeorgis was funded by a DTA from Imperial College London. The work of S. Zafeiriou was funded by the FiDiPro program of Tekes (project number: 1849/31/2015).
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