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Interlinked Convolutional Neural Networks for Face Parsing

  • Yisu Zhou
  • Xiaolin Hu
  • Bo Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

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.

Keywords

Convolutional neural network face parsing deep learning scene labeling 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Yisu Zhou
    • 1
  • Xiaolin Hu
    • 1
  • Bo Zhang
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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