DebFace

Description

An implementation of the paper - "Jointly de-biasing face recognition and demographic attribute estimation" by Sixue Gong et al., 2020 [1]

Directory Structure

Use the tree command to recursively generate the directory structure and insert here.

.
├── adversarial_training.py
├── backbones
│   ├── am_softmax.py
│   ├── classifier.py
│   ├── debface.py
│   ├── encoder.py
│   ├── __init__.py
│   ├── iresnet.py
├── config1.ini
├── config2.ini
├── LICENSE
├── model_summary.txt
├── README.md
├── train_classifiers.py
└── utils
    └── utils_config.py

4 directories, 22 files

The above tree shows the overall structure of repository.

Environment Setup

Dependencies

1. Python 3.6.9
2. PyTorch 1.9.0+cpu
3. torchsummary 1.5.1
4. argparse 1.4.0
5. configparser 5.2.0
6. torchviz 0.0.2

Using host OS environment:

1. Check to see if your Python installation has pip. Enter the following in your terminal:

    pip3 -h
   

   If you see the help text for pip then you have pip installed, otherwise download and install pip

2. Clone the repo from GitHub and then install the various dependencies using pip

   Mac OS / Linux

    git clone https://github.com/hrishi508/DebFace.git
    cd DebFace/
   

Using a virtual environment:

1. Check to see if your Python installation has pip. Enter the following in your terminal:

    pip3 -h
   

   If you see the help text for pip then you have pip installed, otherwise download and install pip

2. Install the virtualenv package

    pip3 install virtualenv
   

3. Create the virtual environment

    virtualenv debface_env
   

4. Activate the virtual environment

   Mac OS / Linux

    source debface_env/bin/activate
   

5. Clone the repo from GitHub and then install the various dependencies using pip

   Mac OS / Linux

    git clone https://github.com/hrishi508/DebFace.git
    cd DebFace/
   

Training the model

Datasets

Running the model

Demo of model inference

<insert, screenrecording and examples of running>

Contributing to the project

Where do I start?

• Ask us by reaching out to any of the contributors through the Contact Us section. Someone there could need help with something.
• You can also take the initiative and fix a bug you found, create an issue for discussion or implement a feature that we never though of, but always wanted.

Ok, I found something. What now?

• Tell us, if you haven't already. Chances are that we have additional information and directions.
• Read the code and get familiar with the engine component you want to work with.
• Do not hesitate to ask us for help if you do not understand something.

How do I contribute my features/changes?

• You can upload work in progress (WIP) revisions or drafts of your contribution to get feedback or support.
• Tell us (again) when you want us to review your work.

Contact us

• Hrishikesh Kusneniwar - hrishi508
• Dr. Sudipta Banerjee - sudban3089

References

[1] Gong, Sixue and Liu, Xiaoming and Jain, A (2020). Jointly de-biasing face recognition and demographic attribute estimation ECCV, link to the paper.