Misalignment-Robust Face Recognition
Abstract
In this paper, we study the problem of subspace-based face recognition under scenarios with spatial misalignments and/or image occlusions. For a given subspace, the embedding of a new datum and the underlying spatial misalignment parameters are simultaneously inferred by solving a constrained ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm optimization problem, which minimizes the error between the misalignment-amended image and the image reconstructed from the given subspace along with its principal complementary subspace. A byproduct of this formulation is the capability to detect the underlying image occlusions. Extensive experiments on spatial misalignment estimation, image occlusion detection, and face recognition with spatial misalignments and image occlusions all validate the effectiveness of our proposed general formulation.
Cite
Text
Wang et al. "Misalignment-Robust Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587804Markdown
[Wang et al. "Misalignment-Robust Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/wang2008cvpr-misalignment/) doi:10.1109/CVPR.2008.4587804BibTeX
@inproceedings{wang2008cvpr-misalignment,
title = {{Misalignment-Robust Face Recognition}},
author = {Wang, Huan and Yan, Shuicheng and Huang, Thomas S. and Liu, Jianzhuang and Tang, Xiaoou},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587804},
url = {https://mlanthology.org/cvpr/2008/wang2008cvpr-misalignment/}
}