Name
Abstract | ||
|---|---|---|
In this paper, a novel classifier named Kernel Nearest-Farthest Subspace (KNFS) classifier is proposed for face recognition. Inspired by the kernel-based classifier and the Nearest-Farthest Subspace (NFS) classifier, KNFS can make the sample points to be linear separable by utilizing the kernel function to map linear inseparable sample points in low-dimensional space to high-dimensional kernel space. And it can improve the recognition accuracy of crossed sample points between classes. The algorithm provides the highest reported recognition accuracy on AR and AT&T database. The results are comparable with many other state-of-art face recognition algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-019-07897-3 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Face recognition, Kernel function, Nearest-farthest subspace classifier | Kernel (linear algebra),Computer vision,Facial recognition system,Pattern recognition,Subspace topology,Computer science,Separable space,Artificial intelligence,Classifier (linguistics),Kernel (statistics) | Journal |
Volume | Issue | ISSN |
78 | 22 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tang Lin-Lin | 1 | 31 | 12.25 |
| Zuohua Li | 2 | 0 | 0.34 |
| Jing-yong Su | 3 | 156 | 10.93 |
| Huifen Lu | 4 | 0 | 0.68 |
| Zhangyan Li | 5 | 0 | 0.68 |
| Zhen Pang | 6 | 0 | 0.68 |
| Yong Zhang | 7 | 1 | 0.69 |