A Simple Explicit Quality Network for Face Recognition

Requirements

• Pytorch 1.8.1

Training Data

1. Download MS1Mv2
2. Extract image files by rec2image.py
3. Generate the training file list

cd dataset
python generate_file_list.py

Test

1. Download pretrained model
2. run test_quality.py

python test_quality.py --backbone backbone.pth --quality quality.path --file test_faces

Training

1. 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 = ''
    ...

2. 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 = ''
    ...

3. 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'
    ...