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Occlusions often occur in face images in the wild, troubling face-relatedtasks such as landmark detection, 3D reconstruction, and face recognition. Itis beneficial to extract face regions from unconstrained face imagesaccurately. However, current face segmentation datasets suffer from small datavolumes, few occlusion types, low resolution, and imprecise annotation,limiting the performance of data-driven-based algorithms. This paper proposes anovel face occlusion dataset with manually labeled face occlusions from theCelebA-HQ and the internet. The occlusion types cover sunglasses, spectacles,hands, masks, scarfs, microphones, etc. To the best of our knowledge, it is byfar the largest and most comprehensive face occlusion dataset. Combining itwith the attribute mask in CelebAMask-HQ, we trained a straightforward facesegmentation model but obtained SOTA performance, convincingly demonstratingthe effectiveness of the proposed dataset.

FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction