Sparse Representation for Face Recognition Based on Discriminative Low-Rank Dictionary Learning
Abstract
In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm.
Cite
Text
Ma et al. "Sparse Representation for Face Recognition Based on Discriminative Low-Rank Dictionary Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247977Markdown
[Ma et al. "Sparse Representation for Face Recognition Based on Discriminative Low-Rank Dictionary Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/ma2012cvpr-sparse/) doi:10.1109/CVPR.2012.6247977BibTeX
@inproceedings{ma2012cvpr-sparse,
title = {{Sparse Representation for Face Recognition Based on Discriminative Low-Rank Dictionary Learning}},
author = {Ma, Long and Wang, Chunheng and Xiao, Baihua and Zhou, Wen},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2012},
pages = {2586-2593},
doi = {10.1109/CVPR.2012.6247977},
url = {https://mlanthology.org/cvpr/2012/ma2012cvpr-sparse/}
}