Accurate Facial Image Parsing at Real-Time Speed

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Abstract
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 loss 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.
Funding Information
  • National Natural Science Foundation of China (U1536203)
  • National Natural Science Foundation of China (61572493)
  • National Natural Science Foundation of China (61876177)
  • National Key Research and Development Program of China (2016QY01W0200)
  • Major Scientific and Technological Project of Hubei Province (2018AAA068)

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