MaskTheFace is computer vision based scripts to mask faces in images. It provides a number of masks to select from.
It is difficult to collect mask dataset under various conditions. MaskTheFace can be used to convert any existing face dataset to masked-face dataset.
MaskTheFace identifies all the faces within an image, and applies the user selected masks to them taking into account various limitations such as face angle, mask fit, lighting conditions etc.
A single image, or entire directory of images can be used as input to code.
It’s advisable to make a new virtual environment and install the dependencies. Following steps can be taken to download get started with MaskTheFace
git clone https://github.com/aqeelanwar/MaskTheFace.git
The provided requirements.txt file can be used to install all the required packages. Use the following command
cd MaskTheFace
pip install –r requirements.txt
This will install the required packages in the activated python environment.
cd MaskTheFace
# Generic
python mask_the_face.py --path <path-to-file-or-dir> --mask_type <type-of-mask> --verbose
# Example
python mask_the_face.py --path 'data/office.jpg' --mask_type 'N95' --verbose
- --path: is used to provide the path of the image file or the directory containing face images.
- --mask_type: is used to select the mask to be applied. Available options are 'N95', 'surgical_blue', 'surgical_green', 'cloth', 'empty' and 'inpaint'. The details of these mask types can be seen in the image above. More masks will be added
- --verbose: used to display useful messages during conversion
Currently MaskTheFace supports the following 4 mask types
- Surgical
- N95
- KN95
- Cloth
New masks are being added. Users, can also add custom masks following the guidelines provided.
Each of the mask types mentioned above can varied in the following terms to create even more masks
MaskTheFace provides 24 existing patterns that can be applied to mask types above to create more variations of the graph. Moreover, users can easily add custom patterns following the guidelines provided.
MaskTheFace provided script to modify existing mask types in terms of colors to generate variations of existing graphs.
MaskTheFace provided script to modify existing mask types in terms of intensity to generate variations of existing graphs.
Face recognition trained to usual face images have proven to give good accuracy.In the recent ongoing outbreak of Covid19, people have been advised to use face masks. With majority of people using face masks, the face recognition system fails to perform. Due to limited mask images, there is not enough masked data available to train a new system. MaskTheFace can be used to create masked data set from unmasked dataset which is then used to either fine-tune an existing or train a new face recognition system.
A face recognition system consisting of 20 different classes was considered. A VGG16 network was trained on these 20 different classes of un-masked faces from VGGFace2 dataset for face recognition. The network achieved an accuracy of 68.3% on un-masked test dataset. When the same network was tested on the masked test images (obtained from MaskTheFace) gave an accuracy of only 36.6% (about half of that of before)
MaskTheFace was used to convert the training dataset from previous problem to masked dataset. Both the unmasked and masked dataset was made a part of training set. The network trained on this dataset gave test accuracy of 70.7% on un-masked, while 65.5% on masked dataset.
Not only the accuracy of masked dataset was improved, but the system also performed better on masked faces.
MaskTheFace generated datasets can be used to monitor if people are using face masks.
The detector above was trained on 2000 images (1000 mask, 1000 without mask) from the VGGface2 dataset. The masked images contained 4 different types of masks. A VGG16 network was trained on these images which achieved a 98.9% accuracy on test dataset.
If you find this repository useful, please use following citation
https://github.com/aqeelanwar/MaskTheFace
