Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel approach called coupled kernel fisher discriminative analysis (CKFDA) based on simultaneous discriminant analysis (SDA) for LR face recognition. Firstly, the high-resolution (HR) and low-resolution (LR) training samples are respectively mapped into two different high-dimensional feature spaces by using kernel functions. Then CKFDA learns two mappings from the kernel images to a common subspace where discrimination property is maximized. Finally, similarity measure is used for classification. Experiments are conducted on publicly available databases to demonstrate the efficacy of our algorithm. © Springer International Publishing 2013. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-02961-0_10 | CCBR |
Keywords | Field | DocType |
face recognition,kernel,linear discriminative analysis | Kernel (linear algebra),Facial recognition system,Similarity measure,Pattern recognition,Computer science,Kernel Fisher discriminant analysis,Artificial intelligence,Linear discriminant analysis,Fisher kernel,Discriminative model,Kernel (statistics) | Conference |
Volume | Issue | ISSN |
8232 LNCS | null | 16113349 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoying Wang | 1 | 1 | 0.35 |
| Le Liu | 2 | 70 | 11.08 |
| Haifeng Hu | 3 | 270 | 60.38 |