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Landmark Detection

This project contains three landmark detection algorithms, implemented in PyTorch.

  • Style Aggregated Network for Facial Landmark Detection, CVPR 2018
  • Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors, CVPR 2018
  • Teacher Supervises Students How to Learn from Partially Labeled Images for Facial Landmark Detection, ICCV 2019
  • Supervision by Registration and Triangulation for Landmark Detection, TPAMI 2020

Style Aggregated Network for Facial Landmark Detection

The training and testing codes for SAN (CVPR 2018) are located in the SAN directory.

Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

The training and testing codes for Supervision-by-Registration (CVPR 2018) are located in the SBR directory.

Teacher Supervises Students How to Learn from Partially Labeled Images for Facial Landmark Detection

The model codes for Teacher Supervises Students (TS3) (ICCV 2019) are located in the TS3 directory.

Supervision by Registration and Triangulation for Landmark Detection

The training and testing codes for SRT (TPAMI) 2020 are located in the SRT directory.

Citation

If this project helps your research, please cite the following papers:

@inproceedings{dong2018san,
   title={Style Aggregated Network for Facial Landmark Detection},
   author={Dong, Xuanyi and Yan, Yan and Ouyang, Wanli and Yang, Yi},
   booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   pages={379--388},
   doi={10.1109/CVPR.2018.00047},
   year={2018}
}
@inproceedings{dong2018sbr,
  title={{Supervision-by-Registration}: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors},
  author={Dong, Xuanyi and Yu, Shoou-I and Weng, Xinshuo and Wei, Shih-En and Yang, Yi and Sheikh, Yaser},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={360--368},
  doi={10.1109/CVPR.2018.00045},
  year={2018}
}
@inproceedings{dong2019teacher,
  title={Teacher Supervises Students How to Learn from Partially Labeled Images for Facial Landmark Detection},
  author={Dong, Xuanyi and Yang, Yi},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  year={2019}
}
@inproceedings{dong2020srt,
  title     = {Supervision by Registration and Triangulation for Landmark Detection},
  author    = {Dong, Xuanyi and Yang, Yi and Wei, Shih-En and Weng, Xinshuo and Sheikh, Yaser and Yu, Shoou-I},
  journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  volume    = {},
  number    = {},
  keywords  = {Landmark Detection;Optical Flow;Triangulation;Deep Learning},
  doi       = {10.1109/TPAMI.2020.2983935},
  ISSN      = {1939-3539},
  year      = {2020},
  month     = {},
  note      = {\mbox{doi}:\url{10.1109/TPAMI.2020.2983935}}
}

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To ask questions or report issues, please open an issue on the issues tracker.

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Four landmark detection algorithms, implemented in PyTorch.

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  • Python 81.7%
  • Shell 18.2%
  • C++ 0.1%