Faster than Real-Time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses

@article{Bhagavatula2017FasterTR,
  title={Faster than Real-Time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses},
  author={Chandraskehar Bhagavatula and Chenchen Zhu and Khoa Luu and Marios Savvides},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={4000-4009}
}
Facial alignment involves finding a set of landmark points on an image with a known semantic meaning. However, this semantic meaning of landmark points is often lost in 2D approaches where landmarks are either moved to visible boundaries or ignored as the pose of the face changes. In order to extract consistent alignment points across large poses, the 3D structure of the face must be considered in the alignment step. However, extracting a 3D structure from a single 2D image usually requires… CONTINUE READING

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