Name
Abstract | ||
|---|---|---|
In finger vein verification, the most important and challenging part is to robustly extract finger vein patterns from low-contrast infrared finger images with limited a priori knowledge. Although recent convolutional neural network (CNN)-based methods for finger vein verification have shown powerful capacity for feature representation and promising perspective in this area, they still have two cri... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIFS.2019.2902819 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
Veins,Gallium nitride,Feature extraction,Training,Generative adversarial networks,Generators,Skin | Computer vision,Joint probability distribution,Pattern recognition,Computer science,Convolutional neural network,Word error rate,A priori and a posteriori,Outlier,Feature extraction,Robustness (computer science),Artificial intelligence,Image resolution | Journal |
Volume | Issue | ISSN |
14 | 9 | 1556-6013 |
Citations | PageRank | References |
6 | 0.41 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| WM | 1 | 221 | 34.28 |
| Changqing Hui | 2 | 6 | 0.41 |
| Zhiquan Chen | 3 | 6 | 0.41 |
| Jing-Hao Xue | 4 | 393 | 46.48 |
| QM | 5 | 464 | 72.05 |