Corpus ID: 235352538

Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition

@article{Wang2021ExploringAL,
  title={Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition},
  author={Shangfei Wang and Ya Chang and Guozhu Peng and Bowen Pan},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.02258}
}
Current works formulate facial action unit (AU) recognition as a supervised learning problem, requiring fully AU-labeled facial images during training. It is challenging if not impossible to provide AU annotations for large numbers of facial images. Fortunately, AUs appear on all facial images, whether manually labeled or not, satisfy the underlying anatomic mechanisms and human behavioral habits. In this paper, we propose a deep semisupervised framework for facial action unit recognition from… Expand

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References

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TLDR
This work proposes to learn label distribution from the ground-truth AU labels, and then train the AU classifiers from the large-scale facial images by maximizing the log likelihood of the mapping functions of AUs with regard to the learnt label distribution for all training data and minimizing the error between predicted AUs and ground- Truth AUs for labeled data simultaneously. Expand
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TLDR
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