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

Interlinked Convolutional Neural Networks for Face Parsing