Name
Abstract | ||
|---|---|---|
Recently low-rank matrix decomposition (LR) and sparse representation classification (SRC) have been successfully applied to address the problem of face recognition. Low-rank matrix decomposition is employed as the first step of robust principal component analysis (RPCA), it is robust to illumination-contaminated image data. In this paper, we propose a novel method based on low-rank decomposition and sparse representation classification which is more robust to illumination-contaminated data. This method is a kind of test-data-drive illumination-robust face recognition. Our experimental results demonstrate the effectiveness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IWSSIP.2017.7965577 | 2017 International Conference on Systems, Signals and Image Processing (IWSSIP) |
Keywords | Field | DocType |
Face recognition,Low-rank matrix decomposition,Sparse representation classification | Computer vision,Facial recognition system,Sparse PCA,Eigenface,Pattern recognition,Computer science,Sparse approximation,Matrix decomposition,Robust principal component analysis,Artificial intelligence,Sparse matrix,Principal component analysis | Conference |
ISSN | ISBN | Citations |
2157-8672 | 978-1-5090-6345-1 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Xu | 1 | 50 | 18.31 |
| Zongqing Lu | 2 | 209 | 26.18 |
| Weifeng Li | 3 | 136 | 22.50 |
| QM | 4 | 464 | 72.05 |