  In this work, we address the face parsing task with a Fully-Convolutional
continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous
face parsing methods that apply region-based subnetwork hundreds of times, our
FC-CNN is fully convolutional with high segmentation accuracy. To achieve this
goal, FC-CNN integrates three subnetworks, a unary network, a pairwise network
and a continuous Conditional Random Field (C-CRF) network into a unified
framework. The high-level semantic information and low-level details across
different convolutional layers are captured by the convolutional and
deconvolutional structures in the unary network. The semantic edge context is
learnt by the pairwise network branch to construct pixel-wise affinity. Based
on a differentiable superpixel pooling layer and a differentiable C-CRF layer,
the unary network and pairwise network are combined via a novel continuous CRF
network to achieve spatial consistency in both training and test procedure of a
deep neural network. Comprehensive evaluations on LFW-PL and HELEN datasets
demonstrate that FC-CNN achieves better performance over the other
state-of-arts for accurate face labeling on challenging images.
