Name
Abstract | ||
|---|---|---|
Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces vi... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2018.2883743 | IEEE Transactions on Image Processing |
Keywords | DocType | Volume |
Face,Face recognition,Feature extraction,Image resolution,Computational modeling,Image coding,Facial features | Journal | 28 |
Issue | ISSN | Citations |
4 | 1057-7149 | 18 |
PageRank | References | Authors |
0.68 | 48 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiming Ge | 1 | 106 | 24.60 |
| Shengwei Zhao | 2 | 31 | 4.67 |
| Chenyu Li | 3 | 19 | 1.03 |
| Jia Li | 4 | 524 | 42.09 |