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Fine-grained semantic segmentation of a person's face and head, includingfacial parts and head components, has progressed a great deal in recent years.However, it remains a challenging task, whereby considering ambiguousocclusions and large pose variations are particularly difficult. To overcomethese difficulties, we propose a novel framework termed Mask-FPAN. It uses ade-occlusion module that learns to parse occluded faces in a semi-supervisedway. In particular, face landmark localization, face occlusionstimations, anddetected head poses are taken into account. A 3D morphable face model combinedwith the UV GAN improves the robustness of 2D face parsing. In addition, weintroduce two new datasets named FaceOccMask-HQ and CelebAMaskOcc-HQ for faceparing work. The proposed Mask-FPAN framework addresses the face parsingproblem in the wild and shows significant performance improvements with MIOUfrom 0.7353 to 0.9013 compared to the state-of-the-art on challenging facedatasets.

Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN