The global spread of the COVID-19 coronavirus pandemic has led to numerous consequences in our everyday life, including the emergency of a social distance and a mask culture. This repository are proposed the annotation for MAsked FAce (MAFA (Ge et al., 2017)) suitable for the implementation of methods for tracking the presence/absence of a protective mask on people's faces.
MAFA contains 30811 images, 35806 detected faces belong to the masked face class. MAFA was divided into train and test sets. Train set contains 25876 images files, test set - 4935 images files.
Despite the fact that to date the MAFA database is the largest among counterparts, it has a serious drawback: presented labels are incorrect since any overlap of the face is considered as the masked face class, it is also worth noting that there are no clear labels, for example, protective mask, microphone or clown nose, which means that for more correct use of it, repeated manual annotation is necessary. The annotation of the train and test sets were performed according to the following rules:
− Unmasked face, i.e. mouth and nose are opened (0 class);
− Masked face, i.e. mouth and nose are closed with a protective mask (1 class);
− Incorrectly masked face, i.e. mouth or nose is closed with a protective mask (2 class);
− Overlap with another object, i.e. it can be a phone, a fruit, a book, a hand, etc. (3 class).
The protective mask class includes various medical masks, respirators, colorful masks, scarves, etc. This rules are proposed in paper (Ruymina al., 2021).
| Set | 0 class | 1 class | 2 class | 3 class |
|---|---|---|---|---|
| Train | 1644 | 23889 | 715 | 3204 |
| Test full | 2470 | 5140 | 192 | 2203 |
| Test short (is used in Ruymina al., 2021) | 447 | 3707 | 128 | 653 |
- Ge, S., Li, J., Ye, Q., Luo, Z., 2017: Detecting masked faces in the wild with lle-cnns. IEEE Conference on Computer Vision and Pattern Recognition, CVPR-2017, Honolulu, HI, 2682-2690.
- Ryumina, E., Ryumin, D., Ivanko, D., Karpov, A., 2021: A Novel Method for Protective Face Mask Detection Using Con-volutional Neural Networks and Image Histograms. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, ISPRS Archives, XLIV-2/W1-2021, 177-182. doi.org/10.5194/isprs-archives-XLIV-2-W1-2021-177-2021.