- MxNet
- Python3
- Necessary Library for Building Mxnet
One webserver started will be served on localnet, all registered face will be viewed.
Upload face into database to do recognize.
You can use $INSIGHTFACE/src/eval/verification.py to test all the pre-trained models.
Please check Model-Zoo for more pretrained models.
A combined margin method was proposed as a function of target logits value and original θ:
COM(θ) = cos(m_1*θ+m_2) - m_3
For training with m1=1.0, m2=0.3, m3=0.2, run following command:
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network r100 --loss combined --dataset emore
Results by using MS1M-IBUG(MS1M-V1)
| Method | m1 | m2 | m3 | LFW | CFP-FP | AgeDB-30 |
|---|---|---|---|---|---|---|
| W&F Norm Softmax | 1 | 0 | 0 | 99.28 | 88.50 | 95.13 |
| SphereFace | 1.5 | 0 | 0 | 99.76 | 94.17 | 97.30 |
| CosineFace | 1 | 0 | 0.35 | 99.80 | 94.4 | 97.91 |
| ArcFace | 1 | 0.5 | 0 | 99.83 | 94.04 | 98.08 |
| Combined Margin | 1.2 | 0.4 | 0 | 99.80 | 94.08 | 98.05 |
| Combined Margin | 1.1 | 0 | 0.35 | 99.81 | 94.50 | 98.08 |
| Combined Margin | 1 | 0.3 | 0.2 | 99.83 | 94.51 | 98.13 |
| Combined Margin | 0.9 | 0.4 | 0.15 | 99.83 | 94.20 | 98.16 |
If you find InsightFace useful in your research, please consider to cite the following related papers:
@inproceedings{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
booktitle={CVPR},
year={2019}
}