EQFace: An implementation of EQFace: A Simple Explicit Quality Network for Face Recognition

Related tags

Computer Vision FaceQuality
Overview

EQFace: A Simple Explicit Quality Network for Face Recognition

The first face recognition network that generates explicit face quality online. It enables a lot of applications where face quality is used.

Face Quality Result

License

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

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

Training pipeline

  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 = ''
    ...
  1. 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 = ''
    ...
  1. 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'
    ...

Performance Benchmark

Face verification on still image and TF video datasets 1:1 verification on IJB-B and IJB-C datasets 1:N identification on IJB-B and IJB-C datasets

Citation

  • 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}
}
Comments
  • 训练自己的数据集时遇到了问题

    训练自己的数据集时遇到了问题

    File "train_quality.py", line 179, in train() File "train_quality.py", line 127, in train confidence = QUALITY(fc) File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/opt/conda/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 155, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "/opt/conda/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 165, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "/opt/conda/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 85, in parallel_apply output.reraise() File "/opt/conda/lib/python3.6/site-packages/torch/_utils.py", line 395, in reraise raise self.exc_type(msg) RuntimeError: Caught RuntimeError in replica 0 on device 0.

    File "/opt/conda/lib/python3.6/site-packages/torch/nn/parallel/parallel_apply.py", line 60, in _worker output = module(*input, **kwargs) File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/tmp/facequality/models/model_resnet.py", line 110, in forward x = self.qualtiy(x) File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/container.py", line 117, in forward input = module(input) File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 91, in forward return F.linear(input, self.weight, self.bias) File "/opt/conda/lib/python3.6/site-packages/torch/nn/functional.py", line 1676, in linear output = input.matmul(weight.t()) RuntimeError: mat1 dim 1 must match mat2 dim 0

    opened by dingweichao123 2
  • License?

    License?

    Thanks for sharing this very interesting work. Right now, no license has been included in the repo, meaning all rights are reserved (https://opensource.stackexchange.com/questions/1720/what-can-i-assume-if-a-publicly-published-project-has-no-license), and it cannot be used by others.

    Could you perhaps consider including some form of open-source license?

    opened by yhenon 2
  • No Checkpoint Found at 'quality.path' Please Have a Check or Continue to Train from Scratch

    No Checkpoint Found at 'quality.path' Please Have a Check or Continue to Train from Scratch

    Hi, I've downloaded your pretrained model from google drive, but when i run test_quality.py, it returns mentioned error. what should I do to solve it? should I train it from scratch? thanks in advance

    opened by hamid-mp 1
  • Learning rate scheduler doesn't match as stated in the paper.

    Learning rate scheduler doesn't match as stated in the paper.

    In the paper, you said that it would be decayed by 10 after 30, 60, 90 epoch for total of 100 epochs. But in the code, I saw that you were using CosineAnnealingRate, which doesn't have the effect as the above. And also, I saw that u pass T_max hard-code 10 epochs (10 * len(train_loader)) -> is this intentional? Cause this would make the LR varies in a cyclical way. Thank you for reading and answering.

    opened by v-trungdt21 0
  • About training in step 2 anhd step 3

    About training in step 2 anhd step 3

    I see the config in step 2 is: ... 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 = '' ... So where can i get the backbone_resume.pth and head_resume.pth And where can i get pretrained_backbone_resume.pth and pretrained_qulity_resume.pth in step 3? ... 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' ...

    opened by huytuong010101 0
  • How to train with another backbone?

    How to train with another backbone?

    Thank you for perfect repository <3 I see that you provided code to training model Arcface using Resnet backbone? So I want to train with MobileFacenet backbone. Can you help me resolve this problem? Thank you <3

    opened by huytuong010101 0
  • about Implementation

    about Implementation

    Hello,I am very interested in your work.and I want to ask some questions, why is the implementation of the code inconsistent with the description in the paper, such as training parameters, learning rate strategy, etc.

    opened by WWWJL 7
Owner
DeepCam Shenzhen
DeepCam Shenzhen
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