4.7 Article

Accurate Facial Image Parsing at Real-Time Speed

Journal

IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 28, Issue 9, Pages 4659-4670

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2019.2909652

Keywords

Face parsing; receptive field; metrics learning; distillation; deep learning

Funding

  1. Natural Science Foundation of China [U1536203, 61572493, 61876177]
  2. National Key Research and Development Program of China [2016QY01W0200]
  3. Major Scientific and Technological Project of Hubei Province [2018AAA068]

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In this paper, we propose a design scheme for deep learning networks in the face parsing task with promising accuracy and real-time inference speed. By analyzing the differences between the general image parsing task and face parsing task, we first revisit the structure of traditional FCN and make improvements to adapt to the unique properties of the face parsing task. Especially, the concept of Normalized Receptive Field is proposed to give more insights on designing the network. Then, a novel lass function called Statistical Contextual Loss is introduced, which integrates richer contextual information and regularizes features during training. For further model acceleration, we propose a semi-supervised distillation scheme that effectively transfers the learned knowledge to a lighter network. Extensive experiments on LFW and Helen dataset demonstrate the significant superiority of the new design scheme on both efficacy and efficiency.

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