The first face recognition network that generates explicit face quality online. It enables a lot of applications where face quality is used.
The code of EQFace is released under the MIT License. There is no limitation for both academic and commercial usage.
- Pytorch 1.8.1
- Download MS1Mv2
- Extract image files by rec2image.py
- Generate the training file list
cd dataset
python generate_file_list.py
- Download pretrained model
- run test_quality.py
python test_quality.py --backbone backbone.pth --quality quality.path --file test_faces
- Step 1: set config.py, then run python train_feature.py
...
BACKBONE_RESUME_ROOT = ''
HEAD_RESUME_ROOT = ''
TRAIN_FILES = './dataset/face_train_ms1mv2.txt'
BACKBONE_LR = 0.05
PRETRAINED_BACKBONE = ''
PRETRAINED_QUALITY = ''
...- Step 2: set config.py, then run python train_quality.py
...
BACKBONE_RESUME_ROOT = './backbone_resume.pth'
HEAD_RESUME_ROOT = './head_resume.pth'
TRAIN_FILES = './dataset/face_train_ms1mv2.txt'
BACKBONE_LR = 0.05
PRETRAINED_BACKBONE = ''
PRETRAINED_QUALITY = ''
...- Step 3: set config.py, then run python train_feature.py
...
BACKBONE_RESUME_ROOT = ''
HEAD_RESUME_ROOT = ''
TRAIN_FILES = './dataset/face_train_ms1mv2.txt'
BACKBONE_LR = 0.05
PRETRAINED_BACKBONE = ''
PRETRAINED_QUALITY = ''
PRETRAINED_BACKBONE = 'pretrained_backbone_resume.pth'
PRETRAINED_QUALITY = 'pretrained_qulity_resume.pth'
...
-
If you think this work is useful for you, please cite
@inproceedings{EQFace, title = {EQFace: A Simple Explicit Quality Network for Face Recognition}, author = {Liu, Rushuai and Tan, Weijun}, booktitle = {CVPRW}, year = {2021} }
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