Real-Time Facial Feature Detection Using Conditional Regression Forests
Abstract
Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patches and the location of feature points from the entire set of faces, conditional regression forest learn the relations conditional to global face properties. In our experiments, we use the head pose as a global property and demonstrate that conditional regression forests outperform regression forests for facial feature detection. We have evaluated the method on the challenging Labeled Faces in the Wild [20] database where close-to-human accuracy is achieved while processing images in real-time. © 2012 IEEE.
Cite
Text
Dantone et al. "Real-Time Facial Feature Detection Using Conditional Regression Forests." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247976Markdown
[Dantone et al. "Real-Time Facial Feature Detection Using Conditional Regression Forests." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/dantone2012cvpr-real/) doi:10.1109/CVPR.2012.6247976BibTeX
@inproceedings{dantone2012cvpr-real,
title = {{Real-Time Facial Feature Detection Using Conditional Regression Forests}},
author = {Dantone, Matthias and Gall, Juergen and Fanelli, Gabriele and Van Gool, Luc},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2012},
pages = {2578-2585},
doi = {10.1109/CVPR.2012.6247976},
url = {https://mlanthology.org/cvpr/2012/dantone2012cvpr-real/}
}