InsightFace - Face Recognition

Requirements

1. MxNet
2. Python3
3. Necessary Library for Building Mxnet

Starting Webserver

One webserver started will be served on localnet, all registered face will be viewed.

Viewing inference Result

Adding Face and Training

Upload face into database to do recognize.

Pretrained Models

You can use $INSIGHTFACE/src/eval/verification.py to test all the pre-trained models.

Please check Model-Zoo for more pretrained models.

Verification Results on Combined Margin

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

Citation

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}
}