Name
Abstract | ||
|---|---|---|
Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, it is still difficult for CNNs to be applied to vein recognition tasks due to the problems of insufficient training datasets, intra-class variations, and inter-class similarities. Besides, due to the essential requirement on the storage of millions of parameters for CNN, it is challengi... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIFS.2019.2924553 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
Veins,Training,Data models,Feature extraction,Task analysis,Image recognition,Biological system modeling | Data modeling,Pattern recognition,Task analysis,Computer science,Convolutional neural network,Identification system,Feature extraction,Low-rank approximation,Redundancy (engineering),Artificial intelligence,Discriminative model | Journal |
Volume | Issue | ISSN |
15 | 1 | 1556-6013 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoqing Wang | 1 | 75 | 17.84 |
| Changming Sun | 2 | 895 | 88.21 |
| Arcot Sowmya | 3 | 319 | 60.05 |