Square Loss Based Regularized LDA for Face Recognition Using Image Sets
Abstract
In this paper, we focus on face recognition over image sets, where each set is represented by a linear subspace. Linear Discriminant Analysis (LDA) is adopted for discriminative learning. After investigating the relation between regularization on Fisher Criterion and Maximum Margin Criterion, we present a unified framework for regularized LDA. With the framework, the ratio-form maximization of regularized Fisher LDA can be reduced to the difference-form optimization with an additional constraint. By incorporating the empirical loss as the regularization term, we introduce a generalized Square Loss based Regularized LDA (SLR-LDA) with suggestion on parameter setting. Our approach achieves superior performance to the state-of-the-art methods on face recognition. Its effectiveness is also evidently verified in general object and object category recognition experiments.
Cite
Text
Geng et al. "Square Loss Based Regularized LDA for Face Recognition Using Image Sets." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204307Markdown
[Geng et al. "Square Loss Based Regularized LDA for Face Recognition Using Image Sets." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/geng2009cvprw-square/) doi:10.1109/CVPRW.2009.5204307BibTeX
@inproceedings{geng2009cvprw-square,
title = {{Square Loss Based Regularized LDA for Face Recognition Using Image Sets}},
author = {Geng, Yanlin and Shan, Caifeng and Hao, Pengwei},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2009},
pages = {99-106},
doi = {10.1109/CVPRW.2009.5204307},
url = {https://mlanthology.org/cvprw/2009/geng2009cvprw-square/}
}