WearMask3D

This repository contains code for fitting masks to face images in the wild by utilizing the 3D morphable model. The code is provided without any warranty. If using this code, please cite our work as shown below.

@inproceedings{wearmask3d, 
    author = {Hong*, Je Hyeong and Kim*, Hanjo and Kim, Minsoo and Nam, Gi Pyo and Cho, Junghyun and Ko, Hyeong-Seok and Kim, Ig-Jae}, 
    booktitle = {2021 IEEE Conference on Image Processing (ICIP)}, 
    title = {A {3D} model-based approach for fitting masks to faces in the wild}, 
    year = {2021}, 
}

How to run WearMask3D

1. Download the dlib landmark pre-trained model from Jianzhu Guo's Google Drive and store the file in the models directory.

2. Install the requirements listed in requirements.txt, e.g. by typing

pip install -r requirements.txt

3. Run the demo code by running

python main.py

Parameters

Below is a list of tunable hyperparameters.

Source path: Path of the input facial images

"srcPath": "./src"

Destination path: Save path of the mask-augmented output images

"dstPath": "./dst"

Version: Version of the WearMask3D's mask augmentation method. You can choose "1.1" or "1.0". ("1.1" is recommended.)

"version":  "1.1"

Brightness threshold: Pre-determined threshold value of the brightness of the input image. If an input image has a lower brightness than the threshold value, the rendered mask image becomes darker.

"brightnessThreshold": 0.4

Variance of the Laplacian threshold: Pre-determined threshold value of the variance of the Laplacian of the input image. If the Laplacian variance of the input image is lower than the threshold, the resolution of the mask image is lowered.

"laplacianVarianceThreshold":  400

Masked Faces in the Wild (MFW-mini)

You can find a list of images here If you wish to obtain a cropped copy of the images, please email kface-at-imrc.kist.re.kr.

Bug report

Please raise an issue on Github for issues related to this code.