The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature. However, at the moment, there are no available large dataset of masked face images that permits to check if detected masked faces are correctly worn or not. Indeed, many people are not correctly wearing their masks due to bad practices, bad behaviors or vulnerability of individuals (e.g., children, old people). For these reasons, several mask wearing campaigns intend to sensitize people about this problem and good practices. In this sense, this work proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net). Realistic masked face datasets are proposed with a twofold objective: i) to detect people having their faces masked or not masked, ii) to detect faces having their masks correctly worn or incorrectly worn (e.g.; at airport portals or in crowds). To the best of our knowledge, no large dataset of masked faces provides such a granularity of classification towards permitting mask wearing analysis. Moreover, this work globally presents the applied mask-to-face deformable model for permitting the generation of other masked face images, notably with specific masks. Our datasets of masked face images (137,016 images) are available at https://github.com/cabani/MaskedFace-Net.

本文提出三种类型的口罩面孔检测数据集，包括正确佩戴口罩的口罩面数据集（CMFD），错误佩戴口罩的口罩面数据集（IMFD）以及用于全球口罩面检测的它们的组合（MaskedFace-Net），这些数据集可用于培训深度学习模型。这些数据集提供了检测佩戴口罩或未佩戴口罩的人脸以及检测戴口罩是否正确（例如在人群中或机场门口）的功能，并具有生成其他口罩面图像的功能。

COVID-19背景下正确/错误佩戴口罩人脸图像数据集MaskedFace-Net