A 3D model-based approach for fitting masks to faces in the wild
This addresses the problem of insufficient training data for masked face recognition, which is crucial for security and surveillance applications, but it is an incremental improvement over existing augmentation techniques.
The authors tackled the lack of labeled masked face images for recognition during the COVID-19 pandemic by developing WearMask3D, a 3D model-based method that generates realistic masked faces from unmasked images, leading to state-of-the-art recognition accuracy.
Face recognition 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 produces more realistic masked faces, and utilizing these images for training leads to state-of-the-art recognition accuracy for masked faces.