COMASK20 DATASET

The COMASK20 was collected for research purpose of our work on recognition with masked face.

For the reference paper: Masked Face Recognition with Convolutional Neural Networks and Local Binary Patterns, please check here.

The repository is now attached with instructions on using the data.

---

Hierarchical dataset structure

.
├─ dataset/
│   ├── person_1/
│   ├── person_2/
│   ├── ...
│   ├── person_N/
├─ README.md

---

Usage

Each folder of individual contains:

• Masked face images: images containing face wearing mask
• Non-masked face images: images containing face without wearing mask

Depend on your purpose, you can split the data into non-masked vs masked images by checking for the term "nomask" on the name of the file. All the files contains this term will be non-masked images and the rest should be masked ones.

# Python

from imutils import paths

all_image_paths = list(paths.list_images(folder))
nomasked_imgs = [x for x in all_image_paths if x.lower().__contains__("nomask")]

% Matlab


all_image_paths = [dir('*.jpg'); dir('*.png'); dir('*.jpeg')];

f=@(x) (append(x.folder, "/", x.name));
full_paths = arrayfun(f, all_image_paths);

nomasked_imgs = full_paths(contains(full_paths, "nomask") == 1);

Citation

Please cite the paper, when you using this dataset

@article{article,
author = {Vu, Hoai and Nguyen, Mai and Pham, Cuong},
year = {2021},
month = {08},
pages = {},
title = {Masked face recognition with convolutional neural networks and local binary patterns},
journal = {Applied Intelligence},
doi = {10.1007/s10489-021-02728-1}
}

Contact

For further expand the data, you are welcome to send me (huongnm.ptit@gmail.com) an email attached with your personal pictures in 2 contexts: with and without mask. We will process the received pictures and add them to the repo afterwards.

Questions related to the algorithm in the paper can also be sent to the same email for more support.