ML Anthology

Towards Robust Cascaded Regression for Face Alignment in the Wild

Chengchao Qu, Hua Gao, Eduardo Monari, Jürgen Beyerer, Jean-Philippe Thiran
CVPRW 2015 pp. 1-9
doi:10.1109/CVPRW.2015.7301348 /cvprw/2015/qu2015cvprw-robust/

Abstract

Most state-of-the-art solutions for localizing facial feature landmarks build on the recent success of the cascaded regression framework [7, 15, 34], which progressively predicts the shape update based on the previous shape estimate and its feature calculation.

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Cite

Text

Qu et al. "Towards Robust Cascaded Regression for Face Alignment in the Wild." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301348

Markdown

[Qu et al. "Towards Robust Cascaded Regression for Face Alignment in the Wild." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/qu2015cvprw-robust/) doi:10.1109/CVPRW.2015.7301348

BibTeX

@inproceedings{qu2015cvprw-robust,
  title     = {{Towards Robust Cascaded Regression for Face Alignment in the Wild}},
  author    = {Qu, Chengchao and Gao, Hua and Monari, Eduardo and Beyerer, Jürgen and Thiran, Jean-Philippe},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2015},
  pages     = {1-9},
  doi       = {10.1109/CVPRW.2015.7301348},
  url       = {https://mlanthology.org/cvprw/2015/qu2015cvprw-robust/}
}