  Face parsing is a basic task in face image analysis. It amounts to labeling
each pixel with appropriate facial parts such as eyes and nose. In the paper,
we present a interlinked convolutional neural network (iCNN) for solving this
problem in an end-to-end fashion. It consists of multiple convolutional neural
networks (CNNs) taking input in different scales. A special interlinking layer
is designed to allow the CNNs to exchange information, enabling them to
integrate local and contextual information efficiently. The hallmark of iCNN is
the extensive use of downsampling and upsampling in the interlinking layers,
while traditional CNNs usually uses downsampling only. A two-stage pipeline is
proposed for face parsing and both stages use iCNN. The first stage localizes
facial parts in the size-reduced image and the second stage labels the pixels
in the identified facial parts in the original image. On a benchmark dataset we
have obtained better results than the state-of-the-art methods.
