J. Deng, Y. Zhou, S. Cheng and S. Zaferiou, "Cascade Multi-View Hourglass Model for Robust 3D Face Alignment," 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi'an, 2018, pp. 399-403. doi: 10.1109/FG.2018.00064
Cascade multi-view hourglass model for robust 3D face alignment
|Author:||Deng, Jiankang1; Zhou, Yuxiang1; Cheng, Shiyang1;|
1Department of Computing, Imperial College London, UK
2Centre for Machine Vision and Signal Analysis, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 6.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201902276500
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-02-27
Estimating the 3D facial landmarks from a 2D image remains a challenging problem. Even though state-of-the-art 2D alignment methods are able to predict accurate landmarks for semi-frontal faces, the majority of them fail to provide semantically consistent landmarks for profile faces. A de facto solution to this problem is through 3D face alignment that preserves correspondence across different poses. In this paper, we proposed a Cascade Multi-view Hourglass Model for 3D face alignment, where the first Hourglass model is explored to jointly predict semi-frontal and profile 2D facial landmarks, after removing spatial transformations, another Hourglass model is employed to estimate the 3D facial shapes. To improve the capacity without sacrificing the computational complexity, the original residual bottleneck block in the Hourglass model is replaced by a parallel, multi-scale inception-resnet block. Extensive experiments on two challenging 3D face alignment datasets, AFLW2000-3D and Menpo-3D, show the robustness of the proposed method under continuous pose changes.
|Pages:||399 - 403|
13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
IEEE International Conference on Automatic Face and Gesture Recognition
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
J. Deng is supported by the President’s Scholarship of Imperial College London. The work of S. Cheng is funded by the EPSRC project EP/N007743/1 (FACER2VM). We thank the NVIDIA Corporation for the GPU donations.
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