Name
Abstract | ||
|---|---|---|
In recent years, multimodal biometrics recognition technology takes more attention by its higher safety and better performance. In this paper, we propose an efficient feature-level fusion algorithm for face and palm. We extract the features of face and palm by principal component analysis(PCA), and then use the canonical correlation analysis(CCA) to carry out feature fusion and get correlation characteristic features. The experiment results show that our method has the better performance than that of two unimodal biometrics and four feature fusion algorithms. © 2013 ISSN 2073-4212. |
Year | DOI | Venue |
|---|---|---|
2013 | null | J. Inf. Hiding Multim. Signal Process. |
Keywords | DocType | Volume |
Canonical correlation analysis,Feature level fusion,Multimodal biometrics | Journal | 4 |
Issue | ISSN | Citations |
4 | 20734239 | 2 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhifang Wang | 1 | 2 | 0.36 |
| Chao Liu | 2 | 2 | 0.70 |
| Taibin Shi | 3 | 2 | 0.36 |
| Qun Ding | 4 | 2 | 0.36 |