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. In this sense, some large datasets of face images with virus-related protection mask are available in the literature; e.g. the MAsked FAces dataset (MAFA) [1] , the Real-World Masked Face Dataset (RMFD 2 ) and a masked face recognition dataset [2] composed of Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD).
Besides, many people are not correctly wearing their masks due to bad practices, bad behaviors or vulnerability of individuals (e.g., children, old people). In this sense, several mask wearing campaigns intend to sensitize people about this problem and good practices [3] [4] [5] [6] . In [7] , a mobile application "CheckYourMask" has been designed towards permitting people to check if their mask is correctly worn or not by taking a selfie. The creation of a dataset with correctly/incorrectly worn mask classes has been suggested. In [8] , a dataset composed of images with individual or multiple masked faces (853 images) has been proposed towards creating detection model taking into account the improperly masked faces. In the present case, we propose a relatively large dataset of 137,016 masked face images that is divided into two masked face categories; correctly worn and incorrectly worn (see samples in Fig.1a and Fig.1b , respectively).
Specifically, 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 (MaskedFace-Net) for the masked face detection (see dataset structure in Fig.2a) . 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 masked correctly worn or incorrectly worn (e.g.; at airport portals or in crowds). To 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. 2 Applied mask-to-face deformable model
The dataset of face images Flickr-Faces-HQ 3 (FFHQ) has been selected as a base for creating an enhanced dataset MaskedFace-Net composed of correctly and incorrectly masked face images. Indeed, FFHQ contains 70000 high-quality images of human faces in PNG images at 1024 × 1024 resolution and is free of use. The FFHQ dataset offers a lot of variety in terms of age, ethnicity, viewpoint, lighting, and image background. It was originally created as a benchmark for generative adversarial networks (GAN) [9] .
Our MaskedFace-Net dataset has been created by defining a mask-to-face deformable model. A pseudo-code of the global principle for generating MaskedFace-Net is shown in Fig.2b with respect to outputs depicted in Fig.2a . For each face image of FFHQ (e.g. Fig. 3a) , Haar feature-based cascade classifiers are applied for detecting a region of interest (detection of face rectangle). Then, a specific key point detector "shape predictor 68 face landmarks 45 " (model derived from [10] ) is applied to the detected region of interest and permits to automatically detect 68 landmarks of the facial structure (see sample in Fig. 3b) . Besides, an image of a conventional face protection mask (single-use blue face protection mask) has been selected as a reference image for the mapping (see sample in Fig. 3c ). For this latter, 12 key points have manually been annotated for delineating the mask area (polygonal area).
At this stage, four types of mask-to-face mapping have been defined with respect to targeted cases (see Fig.2a ), namely mask covering the nose, mouth and chin (i.e. mask correctly worn), mask only covering the nose and mouth; mask only covering mouth and chin and mask under the mouth (i.e. three cases of mask incorrectly worn). For each type of mask-to-face mapping (CMFD, IMFD1, IMFD2 or IMFD3), a subset of 12 facial key points is retained from the 68 landmarks automatically detected; then matched to the 12 mask key points. By this way, the mask can fit specific areas of the face for each targeted case. Hence, a mask-to-face deformable mask model has been created to generate MaskedFace-Net. Moreover, each targeted case can have up to 2 key points of the mask (amongst 12 key points) that have their locations randomly displaced in a limited perimeter. In particular, this tolerance allows to act on the height of the mask on the face and then to bring more realism to the generated dataset.
Finally, a homography transformation which relies on the defined point-to-point correspondence of landmarks between mask image and face image is applied for mapping mask pixels over the targeted facial areas. Instances of produced face landmarks and corresponding mask-to-face mapping are displayed for each type in Fig.4a and Fig.4b, Fig.4c,  Fig.4d and Fig.4e, Fig.4f, Fig.4g, Fig.4h We emphasize that a raw mask-to-face mapping has been applied to the FFHQ dataset. In particular, no images have been filtered according to specific parameters (e.g. age). However, the file naming of MaskedFace-Net includes the one given by the FFHQ dataset. Hence, correspondence in between the FFHQ and MaskedFace-Net can be established towards related filtering.
It is worth mentioning that the minimum age for mask wearing depends on applicable laws in concerned countries. For instance, the mask wearing is compulsory from 6 years old in Spain, 11 years old in France, 12 years old in Belgium under certain conditions [11] . Between 2 and 11 years old, opinions differ [12] . Since FFHQ contains face images of all ages, it is also the case for masked face image of MaskedFace-Net. Such datasets could then be exploited for detecting children in crowds that wear a mask under the recommended limit of age.
A large dataset of 137,016 quality masked face images has been produced and made available online. MaskedFace-Net can be seen as a benchmark dataset for creating machine learning models related to the mask wearing analysis; notably, detecting the presence of mask or not over detected face images, the correct or incorrect wearing for detected masked faces. MaskedFace-Net can then be used for enhancing vision-based monitoring systems towards several applications such as checking the respect of laws related to the mask wearing or generating crowd statistics. Moreover, the method used for the generation of MaskedFace-Net has been described for permitting the generation of masked face images by using other types of mask.
In no case the contributors of this work could be held responsible for any incident when using the MaskedFace-Net dataset or masks.