Accurate Face Alignment Using Shape Constrained Markov Network
Abstract
In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network more robust to local image noise. We propose a hierarchical Condensation algorithm to draw the shape samples efficiently. Specifically, a proposal density incorporating the local face shape is designed to generate more samples close to the image features for accurate alignment, based on a local Markov network search. A constrained regularization algorithm is also developed to weigh favorably those points that are already accurately aligned. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.
Cite
Text
Liang et al. "Accurate Face Alignment Using Shape Constrained Markov Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.45Markdown
[Liang et al. "Accurate Face Alignment Using Shape Constrained Markov Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/liang2006cvpr-accurate/) doi:10.1109/CVPR.2006.45BibTeX
@inproceedings{liang2006cvpr-accurate,
title = {{Accurate Face Alignment Using Shape Constrained Markov Network}},
author = {Liang, Lin and Wen, Fang and Xu, Ying-Qing and Tang, Xiaoou and Shum, Heung-Yeung},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {1313-1319},
doi = {10.1109/CVPR.2006.45},
url = {https://mlanthology.org/cvpr/2006/liang2006cvpr-accurate/}
}