A Deformation and Lighting Insensitive Metric for Face Recognition Based on Dense Correspondences
Abstract
Face recognition is a challenging problem, complicated by variations in pose, expression, lighting, and the passage of time. Significant work has been done to solve each of these problems separately. We consider the problems of lighting and expression variation together, proposing a method that accounts for both variabilities within a single model. We present a novel deformation and lighting insensitive metric to compare images, and we present a novel framework to optimize over this metric to calculate dense correspondences between images. Typical correspondence cost patterns are learned between face image pairs and a Naïve Bayes classifier is applied to improve recognition accuracy. Very promising results are presented on the AR Face Database, and we note that our method can be extended to a broad set of applications.
Cite
Text
Jorstad et al. "A Deformation and Lighting Insensitive Metric for Face Recognition Based on Dense Correspondences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995431Markdown
[Jorstad et al. "A Deformation and Lighting Insensitive Metric for Face Recognition Based on Dense Correspondences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/jorstad2011cvpr-deformation/) doi:10.1109/CVPR.2011.5995431BibTeX
@inproceedings{jorstad2011cvpr-deformation,
title = {{A Deformation and Lighting Insensitive Metric for Face Recognition Based on Dense Correspondences}},
author = {Jorstad, Anne and Jacobs, David and Trouvé, Alain},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2011},
pages = {2353-2360},
doi = {10.1109/CVPR.2011.5995431},
url = {https://mlanthology.org/cvpr/2011/jorstad2011cvpr-deformation/}
}