  Face recognition research now requires a large number of labelled masked face
images in the era of this unprecedented COVID-19 pandemic. Unfortunately, the
rapid spread of the virus has left us little time to prepare for such dataset
in the wild. To circumvent this issue, we present a 3D model-based approach
called WearMask3D for augmenting face images of various poses to the masked
face counterparts. Our method proceeds by first fitting a 3D morphable model on
the input image, second overlaying the mask surface onto the face model and
warping the respective mask texture, and last projecting the 3D mask back to
2D. The mask texture is adapted based on the brightness and resolution of the
input image. By working in 3D, our method can produce more natural masked faces
of diverse poses from a single mask texture. To compare precisely between
different augmentation approaches, we have constructed a dataset comprising
masked and unmasked faces with labels called MFW-mini. Experimental results
demonstrate WearMask3D, which will be made publicly available, produces more
realistic masked images, and utilizing these images for training leads to
improved recognition accuracy of masked faces compared to the state-of-the-art.
