LightQNet

This is a demo code of training and testing [LightQNet] using Tensorflow.

Uncertainty Losses:

• IDQ loss
• PCNet loss

Uncertainty Networks:

• MobileNetv3-Small
• Resnet18

Usage

Preprocessing

Download the MS-Celeb-1M dataset from 1 or 2:

1. insightface, https://github.com/deepinsight/insightface/wiki/Dataset-Zoo
2. face.evoLVe.PyTorch, https://github.com/ZhaoJ9014/face.evoLVe.PyTorch#Data-Zoo)

Decode it using the code: https://github.com/deepinsight/insightface/blob/master/recognition/common/rec2image.py

Training

1. Download the base model ResFace64 from Baidu Drive PW:v800 and unzip the files under log/resface64.

2. Modify the configuration files under configfig/ folder.

3. Start the training:

   python train_idq.py configfig/resface64_msarcface_with_mbv3_small_idq.py

Testing

We use lfw.bin, cfp_fp.bin, etc. from ms1m-retinaface-t1 as the test dataset.

python evaluation/verification_risk_fnmr.py

Freeze and Deploy

Freeze

python freeze_idq.py --model_dir log/resface64_mbv3/20210128-150935

Deployment code

https://github.com/KaenChan/lightqnet

Pre-trained Model

ResFace64

Method	Download
Base Mode	Baidu Drive PW:v800
Mobilenetv3-small + IDQ loss + Distillation	Baidu Drive PW:3zgi

Reference

If you find this repo useful, please consider citing:

@article{chen2021lightqnet,
  title={LightQNet: Lightweight Deep Face Quality Assessment for Risk-Controlled Face Recognition},
  author={Chen, Kai and Yi, Taihe and Lv, Qi},
  journal={IEEE Signal Processing Letters},
  volume={28},
  pages={1878--1882},
  year={2021},
  publisher={IEEE}
}