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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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End-to-end face parsing via interlinked convolutional neural networks.

Zi Yin1, Valentin Yiu2,3, Xiaolin Hu2

  • 1School of Technology, Beijing Forestry University, Beijing, 100083 China.

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|March 31, 2021
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Summary

This study introduces an end-to-end framework for face parsing, enhancing accuracy by integrating a Spatial Transformer Network (STN) into Interlinked Convolutional Neural Networks (iCNN). The STN-aided iCNN (STN-iCNN) enables joint training, significantly improving facial part segmentation performance.

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End-to-endFace parsingSTN-iCNN

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Segmentation

Background:

  • Accurate pixel segmentation of facial parts is crucial for face analysis.
  • The two-stage Interlinked Convolutional Neural Networks (iCNN) model is effective but limited by separate training.
  • Existing methods require separate training stages, hindering overall performance.

Purpose of the Study:

  • To develop a simple, end-to-end face parsing framework.
  • To improve the accuracy and performance of the original iCNN model.
  • To enable joint training of a two-stage face parsing pipeline.

Main Methods:

  • Introduced a Spatial Transformer Network (STN) between the two stages of iCNN.
  • Developed the STN-aided iCNN (STN-iCNN) framework for end-to-end joint training.
  • Leveraged STN for trainable connections and improved part cropping.

Main Results:

  • STN-iCNN significantly improved the accuracy of the original iCNN model.
  • Achieved competitive performance on the Helen Dataset for face parsing.
  • Demonstrated superior performance and good generalization on the CelebAMask-HQ dataset.

Conclusions:

  • The STN-aided iCNN (STN-iCNN) framework enables effective end-to-end joint training for face parsing.
  • This approach enhances segmentation accuracy and model generalization.
  • The proposed method offers a significant advancement in face parsing technology.