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Face parsing infers a pixel-wise label map for each semantic facialcomponent. Previous methods generally work well for uncovered faces, howeveroverlook the facial occlusion and ignore some contextual area outside a singleface, especially when facial occlusion has become a common situation during theCOVID-19 epidemic. Inspired by the illumination theory of image, we propose anovel homogeneous tanh-transforms for image preprocessing, which made up offour tanh-transforms, that fuse the central vision and the peripheral visiontogether. Our proposed method addresses the dilemma of face parsing underocclusion and compresses more information of surrounding context. Based onhomogeneous tanh-transforms, we propose an occlusion-aware convolutional neuralnetwork for occluded face parsing. It combines the information both inTanh-polar space and Tanh-Cartesian space, capable of enhancing receptivefields. Furthermore, we introduce an occlusion-aware loss to focus on theboundaries of occluded regions. The network is simple and flexible, and can betrained end-to-end. To facilitate future research of occluded face parsing, wealso contribute a new cleaned face parsing dataset, which is manually purifiedfrom several academic or industrial datasets, including CelebAMask-HQ,Short-video Face Parsing as well as Helen dataset and will make it public.Experiments demonstrate that our method surpasses state-of-art methods of faceparsing under occlusion.

Occlusion-Aware Deep Convolutional Neural Network via Homogeneous Tanh-transforms for Face Parsing