Face Alignment Using Statistical Models and Wavelet Features
Abstract
Active shape model (ASM) is a powerful statistical tool for face alignment by shape. However, it can suffer from changes in illumination and facial expression changes, and local minima in optimization. In this paper, we present a method, W-ASM, in which Gabor wavelet features are used for modeling local image structure. The magnitude and phase of Gabor features contain rich information about the local structural features of face images to be aligned, and provide accurate guidance for search. To a large extent, this repairs defects in gray scale based search. An E-M algorithm is used to model the Gabor feature distribution, and a coarse-to-fine grained search is used to position local features in the image. Experimental results demonstrate the ability of W-ASM to accurately align and locate facial features.
Cite
Text
Jiao et al. "Face Alignment Using Statistical Models and Wavelet Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003. doi:10.1109/CVPR.2003.1211370Markdown
[Jiao et al. "Face Alignment Using Statistical Models and Wavelet Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003.](https://mlanthology.org/cvpr/2003/jiao2003cvpr-face/) doi:10.1109/CVPR.2003.1211370BibTeX
@inproceedings{jiao2003cvpr-face,
title = {{Face Alignment Using Statistical Models and Wavelet Features}},
author = {Jiao, Feng and Li, Stan Z. and Shum, Heung-Yeung and Schuurmans, Dale},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2003},
pages = {321-327},
doi = {10.1109/CVPR.2003.1211370},
url = {https://mlanthology.org/cvpr/2003/jiao2003cvpr-face/}
}