  The existing face recognition datasets usually lack occlusion samples, which
hinders the development of face recognition. Especially during the COVID-19
coronavirus epidemic, wearing a mask has become an effective means of
preventing the virus spread. Traditional CNN-based face recognition models
trained on existing datasets are almost ineffective for heavy occlusion. To
this end, we pioneer a simulated occlusion face recognition dataset. In
particular, we first collect a variety of glasses and masks as occlusion, and
randomly combine the occlusion attributes (occlusion objects, textures,and
colors) to achieve a large number of more realistic occlusion types. We then
cover them in the proper position of the face image with the normal occlusion
habit. Furthermore, we reasonably combine original normal face images and
occluded face images to form our final dataset, termed as Webface-OCC. It
covers 804,704 face images of 10,575 subjects, with diverse occlusion types to
ensure its diversity and stability. Extensive experiments on public datasets
show that the ArcFace retrained by our dataset significantly outperforms the
state-of-the-arts. Webface-OCC is available at
https://github.com/Baojin-Huang/Webface-OCC.
