Name
Abstract | ||
|---|---|---|
As feature extraction techniques, Kernel Principal Component Analysis (KPCA) and Independent Component Analysis (ICA) can both be considered as generalization of Principal Component Analysis (PCA), which has been used for palmprint recognition and gained satisfactory results [3], therefore it is natural to wonder the performances of KPCA and ICA on this issue. In this paper, palmprint recognition using the KPCA and ICA methods is developed and compared with the PCA method. Based on the experimental results, some useful conclusions are drawn, which fits into the scene for a better picture about considering these unsupervised subspace classifiers for palmprint recognition. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539087_86 | ICNC (1) |
Keywords | Field | DocType |
feature extraction technique,palmprint recognition,pca method,kernel principal,independent component analysis,principal component analysis,unsupervised subspace analysis,better picture,component analysis,ica method,kernel principal component analysis,feature extraction | Vector space,Subspace topology,Pattern recognition,Computer science,Kernel principal component analysis,Feature extraction,Speech recognition,Independent component analysis,Artificial intelligence,Machine learning,Principal component analysis | Conference |
Volume | ISSN | ISBN |
3610 | 0302-9743 | 3-540-28323-4 |
Citations | PageRank | References |
2 | 0.37 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guiyu Feng | 1 | 174 | 9.92 |
| Dewen Hu | 2 | 1290 | 101.20 |
| Ming Li | 3 | 13 | 4.67 |
| Zongtan Zhou | 4 | 412 | 33.89 |