Occlusion-Aware Deep Convolutional Neural Network via Homogeneous
  Tanh-transforms for Face Parsing
Weihua Liu Chaochao Lin Haoping Yu Said Boumaraf Zhaoqiong Pi
Face parsing infers a pixel-wise label map for each semantic facial
component. Previous methods generally work well for uncovered faces, however
overlook the facial occlusion and ignore some contextual area outside a single
face, especially when facial occlusion has become a common situation during the
COVID-19 epidemic. Inspired by the illumination theory of image, we propose a
novel homogeneous tanh-transforms for image preprocessing, which made up of
four tanh-transforms, that fuse the central vision and the peripheral vision
together. Our proposed method addresses the dilemma of face parsing under
occlusion and compresses more information of surrounding context. Based on
homogeneous tanh-transforms, we propose an occlusion-aware convolutional neural
network for occluded face parsing. It combines the information both in
Tanh-polar space and Tanh-Cartesian space, capable of enhancing receptive
fields. Furthermore, we introduce an occlusion-aware loss to focus on the
boundaries of occluded regions. The network is simple and flexible, and can be
trained end-to-end. To facilitate future research of occluded face parsing, we
also contribute a new cleaned face parsing dataset, which is manually purified
from 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 face
parsing under occlusion.