InsightFace

Implementation of Additive Angular Margin Loss for Deep Face Detection. paper.

@article{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
journal={arXiv:1801.07698},
year={2018}
}

DataSet

CASIA WebFace DataSet, 494,414 faces over 10,575 identities.

Dependencies

• PyTorch 1.0.0

Usage

Data wrangling

Extract images, scan them, to get bounding boxes and landmarks:

$ python pre_process.py

Image alignment:

1. Face detection(MTCNN).
2. Face alignment(similar transformation).
3. Central face selection.
4. Resize -> 112x112.

Original	Aligned	Original	Aligned

Train

$ python train.py

To visualize the training process：

$ tensorboard --logdir=runs

Performance evaluation

DataSet

Use Labeled Faces in the Wild (LFW) dataset for performance evaluation:

• 13233 faces
• 5749 identities
• 1680 identities with >=2 photo

Download LFW database put it under data folder:

$ wget http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz
$ wget http://vis-www.cs.umass.edu/lfw/pairs.txt
$ wget http://vis-www.cs.umass.edu/lfw/people.txt

Get it started

$ python lfw_eval.py

Results

Backbones	LFW(%)
SE-LResNet152E-IR	99.43%
SE-LResNet152E-IR	99.38%
SE-LResNet101E-IR	99.27%
LResNet101E-IR	99.23%

theta j Distribution

Error analysis

False Positive

6 false positives:

1	2	1	2

False Negative

28 false negative, these 10 are randomly chosen:

1	2	1	2