  Face parsing aims to predict pixel-wise labels for facial components of a
target face in an image. Existing approaches usually crop the target face from
the input image with respect to a bounding box calculated during
pre-processing, and thus can only parse inner facial Regions of Interest
(RoIs). Peripheral regions like hair are ignored and nearby faces that are
partially included in the bounding box can cause distractions. Moreover, these
methods are only trained and evaluated on near-frontal portrait images and thus
their performance for in-the-wild cases were unexplored. To address these
issues, this paper makes three contributions. First, we introduce iBugMask
dataset for face parsing in the wild containing 1,000 manually annotated images
with large variations in sizes, poses, expressions and background, and
Helen-LP, a large-pose training set containing 21,866 images generated using
head pose augmentation. Second, we propose RoI Tanh-polar transform that warps
the whole image to a Tanh-polar representation with a fixed ratio between the
face area and the context, guided by the target bounding box. The new
representation contains all information in the original image, and allows for
rotation equivariance in the convolutional neural networks (CNNs). Third, we
propose a hybrid residual representation learning block, coined HybridBlock,
that contains convolutional layers in both the Tanh-polar space and the
Tanh-Cartesian space, allowing for receptive fields of different shapes in
CNNs. Through extensive experiments, we show that the proposed method
significantly improves the state-of-the-art for face parsing in the wild.
