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Repository Details

适用于复杂场景的人脸识别身份认证系统

Real-Time ArcFace Multiplex Recognition

Face Detection and Recognition using RetinaFace and ArcFace, can reach nearly 24 fps at GTX1660ti.

ArcFace Demo

How to run

  • Install yarn
    • sudo apt install curl
    • curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
    • echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
    • sudo apt update && sudo apt install yarn
  • Electron Node-JS Client
    • cd electron-client
    • yarn or npm install
    • yarn start or npm start
  • Build R-CNN for Retina Face
    • cd ..
    • chmod a+x ./build_darknet_and_rcnn.sh
    • ./build_rcnn.sh
  • Python Deal
    • python3 usb_camera.py -c X e.g: Replace X with 0
    • Click the corresponding Camera {X} Button at Electron

How to train mlp classifier

  • mkdir ./Temp/raw

  • mkdir ./Temp/train_data

  • Place training pictures in the following format:

    ─── train_data
        ├── bush
        │   ├── 1559637960.1595788.jpg
        │   ├── 1559637960.1762984.jpg
        │   └── 1559637960.2001894.jpg
        ├── clinton
        │   ├── 1559637960.2104468.jpg
        │   ├── 1559637960.2225769.jpg
        │   └── 1559637960.281161.jpg
        └── obama
            ├── 1559637960.2940397.jpg
            ├── 1559637960.31212.jpg
            └── 1559637960.3381834.jpg
  • python3 train_mlp.py

ArcFace Video Demo

ArcFace Demo

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

RetinaFace Introduction

RetinaFace is a practical single-stage SOTA face detector which is initially described in arXiv technical report

demoimg1

demoimg2

Verification

LResNet100E-IR network trained on MS1M-Arcface dataset with ArcFace loss:

Method LFW(%) CFP-FP(%) AgeDB-30(%)
Ours 99.80+ 98.0+ 98.20+

Citation

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

@inproceedings{deng2019retinaface,
    title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
    author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
    booktitle={arxiv},
    year={2019}
}

@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}
}