Name
Abstract | ||
|---|---|---|
Since the appearance of deep learning, face verification (FV) has made great progress with large scale datasets, well-designed networks, new loss functions, fusion of models and metric learning methods. However, incorporating all these methods obviously takes a lot of time both at training and testing stages. In this paper, we just select training images randomly without any clean and alignment procedure. Then we propose a simple weighted average method which combines features of the last two layers with different weights on the modified VGGNet, named as CB-VGG. It is significantly reducing the complexity of time that one model can be treated as two models. LMNN is used as a post-processing procedure to improve the discrimination of the combined features. Our experiments show relatively competitive results on LFW, CFP, and CACD datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-7302-1_2 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Face verification,Deep learning,Weighted average method,LMNN metric learning | Conference | 772 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyu Zhang | 1 | 1 | 1.02 |
| Zhu Jiang | 2 | 44 | 8.16 |
| Mingyu You | 3 | 10 | 2.31 |