NormFace: L2 HyperSphere Embedding for Face Verification
http://www.cs.jhu.edu/~alanlab/Pubs17/wang2017normface.pdf
Baseline Model | Original Accuracy | Finetune Using Normalization |
---|---|---|
Light CNN(MaxOut) | 98.41%* | 98.78% |
Center Face(ResNet) | 99.03% | 99.21% |
* It is 98.13% on Light CNN's project page. After applying the mirror face trick, it becomes 98.41%.
inner_product_layer
, inner_distance_layer
, normalize_layer
, general_contrastive_layer
, flip_layer
and accuracy_layer
to your Caffe. Since there are too many layers to transplant, I suggest you to use my Caffe directly:)0166921
, 1056413
and 1193098
. For fair evaluation, it is recommended to remove them from CAISA-Webface.
Update: Actually there are 17 overlap identities, the list is in my FaceDatasets repository.Evaluation codes are in my another github repository. Please refer to the second paragraph of the Update section.
A trick called mirror face is used during extracting the features. A sample code is in ./prototxt/example_of_mirror_face.prototxt
.
About the mirror face: See ./MirrorFace.md
About the histogram feature for video face verification: Details are in ./YTF.
Light CNN B model(98.78%): Google Drive or Baidu Yun.
ps: I used to get a model of 98.8%, but I lost it....
Center Face (ResNet-27) model(99.21%): Google Drive or Baidu Yun.
This code is distributed under MIT LICENSE. The released models are only allowed for non-commercial use.
If you find my paper or the codes useful in your research, please cite
@inproceedings{wang2017normface,
title={NormFace: L2 Hypersphere Embedding for Face Verification},
author={Wang, Feng and Xiang, Xiang and Cheng, Jian and Yuille, Alan L.},
booktitle={Proceedings of the 25th ACM international conference on Multimedia},
year={2017},
organization={ACM},
doi = {https://doi.org/10.1145/3123266.3123359}
}
Feng Wang [feng.wff(at)gmail.com], please replace (at) with @.