EAC-Net: A Region-Based Deep Enhancing and Cropping Approach for Facial Action Unit Detection

@article{Li2017EACNetAR,
  title={EAC-Net: A Region-Based Deep Enhancing and Cropping Approach for Facial Action Unit Detection},
  author={Wei Li and Farnaz Abtahi and Zhigang Zhu and Lijun Yin},
  journal={2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)},
  year={2017},
  pages={103-110}
}
In this paper, we propose a deep learning basedapproach for facial action unit detection by enhancing andcropping the regions of interest. The approach is implementedby adding two novel nets (layers): the enhancing layers and thecropping layers, to a pretrained CNN model. For the enhancinglayers (the E-Net), we designed an attention map based on faciallandmark features and applied it to a pretrained neural networkto conduct enhanced learning. For the cropping layers (the CNet), we crop facial… CONTINUE READING
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