Name
Abstract | ||
|---|---|---|
UDP has been successfully applied in many fields, finding a subspace that maximizes the ratio of the nonlocal scatter to the local scatter. But UDP can not represent the nonlinear space well because it is a linear method in nature. Kernel methods can otherwise discover the nonlinear structure of the images. To improve the performance of UDP, kernel UDP (a nonlinear vision of UDP) is proposed for face feature extraction and face recognition via kernel tricks in this paper. We formulate the kernel UDP theory and develop a two-stage method to extract kernel UDP features: namely weighted Kernel PCA plus UDP. The experimental results on the FERET and ORL databases show that the proposed kernel UDP is effective. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s11063-011-9190-0 | Neural Processing Letters |
Keywords | Field | DocType |
UDP,Kernel,Feature extraction,Face Recognition | Kernel (linear algebra),Facial recognition system,Nonlinear system,Subspace topology,Pattern recognition,Radial basis function kernel,Computer science,Feature extraction,Kernel principal component analysis,Artificial intelligence,Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 2 | 1370-4621 |
Citations | PageRank | References |
1 | 0.36 | 33 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wankou Yang | 1 | 535 | 34.68 |
| Changyin Sun | 2 | 2002 | 157.17 |
| Jing-yu Yang | 3 | 6061 | 345.83 |
| Helen S. Du | 4 | 162 | 15.20 |
| Karl Ricanek | 5 | 165 | 18.65 |