/insightface

State-of-the-art 2D and 3D Face Analysis Project

Primary LanguagePythonMIT LicenseMIT

InsightFace: 2D and 3D Face Analysis Project

InsightFace project is mainly maintained By Jia Guo and Jiankang Deng.

For all main contributors, please check contributing.

Top News

2021-08-07: Add new model_zoo page.

2021-07-13: We now have implementations based on paddlepaddle: arcface_paddle for face recognition and blazeface_paddle for face detection.

2021-07-09: We add a person_detection example, trained by SCRFD, which can be called directly by our python-library.

2021-06-05: We launch a Masked Face Recognition Challenge & Workshop on ICCV 2021.

2021-05-15: We released an efficient high accuracy face detection approach called SCRFD.

2021-04-18: We achieved Rank-4th on NIST-FRVT 1:1, see leaderboard.

2021-03-13: We have released our official ArcFace PyTorch implementation, see here.

License

The code of InsightFace is released under the MIT License. There is no limitation for both academic and commercial usage.

The training data containing the annotation (and the models trained with these data) are available for non-commercial research purposes only.

Both manual-downloading models from our github repo and auto-downloading models with our python-library follow the above license policy(which is for non-commercial research purposes only).

Introduction

InsightFace is an open source 2D&3D deep face analysis toolbox, mainly based on PyTorch and MXNet.

Please check our website for detail.

The master branch works with PyTorch 1.6+ and/or MXNet=1.6-1.8, with Python 3.x.

InsightFace efficiently implements a rich variety of state of the art algorithms of face recognition, face detection and face alignment, which optimized for both training and deployment.

Quick Start

Please start with our python-package, for testing detection, recognition and alignment models on input images.

ArcFace Video Demo

Please click the image to watch the Youtube video. For Bilibili users, click here.

Projects

The page on InsightFace website also describes all supported projects in InsightFace.

You may also interested in some challenges hold by InsightFace.

Face Recognition

Introduction

In this module, we provide training data, network settings and loss designs for deep face recognition.

The supported methods are as follows:

Commonly used network backbones are included in most of the methods, such as IResNet, MobilefaceNet, MobileNet, InceptionResNet_v2, DenseNet, etc..

Datasets

The training data includes, but not limited to the cleaned MS1M, VGG2 and CASIA-Webface datasets, which were already packed in MXNet binary format. Please dataset page for detail.

Evaluation

We provide standard IJB and Megaface evaluation pipelines in evaluation

Pretrained Models

Please check Model-Zoo for more pretrained models.

Third-party Re-implementation of ArcFace

Face Detection

Introduction

In this module, we provide training data with annotation, network settings and loss designs for face detection training, evaluation and inference.

The supported methods are as follows:

RetinaFace is a practical single-stage face detector which is accepted by CVPR 2020. We provide training code, training dataset, pretrained models and evaluation scripts.

SCRFD is an efficient high accuracy face detection approach which is initialy described in Arxiv. We provide an easy-to-use pipeline to train high efficiency face detectors with NAS supporting.

Face Alignment

Introduction

In this module, we provide datasets and training/inference pipelines for face alignment.

Supported methods:

SDUNets is a heatmap based method which accepted on BMVC.

SimpleRegression provides very lightweight facial landmark models with fast coordinate regression. The input of these models is loose cropped face image while the output is the direct landmark coordinates.

Citation

If you find InsightFace useful in your research, please consider to cite the following related papers:


@article{guo2021sample,
  title={Sample and Computation Redistribution for Efficient Face Detection},
  author={Guo, Jia and Deng, Jiankang and Lattas, Alexandros and Zafeiriou, Stefanos},
  journal={arXiv preprint arXiv:2105.04714},
  year={2021}
}

@inproceedings{an2020partical_fc,
  title={Partial FC: Training 10 Million Identities on a Single Machine},
  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
  Zhang, Debing and Fu Ying},
  booktitle={Arxiv 2010.05222},
  year={2020}
}

@inproceedings{deng2020subcenter,
  title={Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces},
  author={Deng, Jiankang and Guo, Jia and Liu, Tongliang and Gong, Mingming and Zafeiriou, Stefanos},
  booktitle={Proceedings of the IEEE Conference on European Conference on Computer Vision},
  year={2020}
}

@inproceedings{Deng2020CVPR,
title = {RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild},
author = {Deng, Jiankang and Guo, Jia and Ververas, Evangelos and Kotsia, Irene and Zafeiriou, Stefanos},
booktitle = {CVPR},
year = {2020}
}

@inproceedings{guo2018stacked,
  title={Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment},
  author={Guo, Jia and Deng, Jiankang and Xue, Niannan and Zafeiriou, Stefanos},
  booktitle={BMVC},
  year={2018}
}

@article{deng2018menpo,
  title={The Menpo benchmark for multi-pose 2D and 3D facial landmark localisation and tracking},
  author={Deng, Jiankang and Roussos, Anastasios and Chrysos, Grigorios and Ververas, Evangelos and Kotsia, Irene and Shen, Jie and Zafeiriou, Stefanos},
  journal={IJCV},
  year={2018}
}

@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}
}

Contributing

Main contributors: