Name
Title | ||
|---|---|---|
Common Subspace Based Low-Rank And Joint Sparse Representation For Multi-View Face Recognition |
Abstract | ||
|---|---|---|
Multi-view face data are very common in real-world application, since different viewpoints and various types of sensors attempt to better represent face data. However, these data have large pose variation, which dramatically degrades the performance of multi-view face recognition. To address this, we propose a common subspace based low-rank and joint sparse representation (CSLRJSR) method, which provides a framework encompassing divergence mitigation and feature fusion. In CSLRJSR method, common subspace is learnt to bridge the view, then low-rank and joint sparse representation are exploited to learn and then fuse the discriminative features. Experiments on multi-view face dataset demonstrate that CSLRJSR outperforms the state-of-the-art methods both in two-view and multi-view situations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-34113-8_13 | IMAGE AND GRAPHICS, ICIG 2019, PT III |
Keywords | DocType | Volume |
Multi-view face recognition, Common subspace, Low-rank representation, Joint sparse | Conference | 11903 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziqiang Wang | 1 | 17 | 15.19 |
| Yingzhi Ouyang | 2 | 0 | 0.34 |
| Weidan Zhu | 3 | 1 | 0.69 |
| Bin Sun | 4 | 4 | 1.07 |
| Qiang Liu | 5 | 0 | 0.34 |