FCSR-GAN

This is the source code for paper

"FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning"

Experiment result

Environment requirest

This code is based on Pytorch 0.4.1 and CUDA 8.0.

Pre-processing

All the faces are processed using the SeetaFace Engineer and GFC.

Dataset

We use the standard train&test split of the CelebA dataset.

License

This project is released under the Apache 2.0 license.

Citation

If you find this work useful, please cite our papers with the following bibtex:

@article{FCSRGAN_TBIOM,
  title   = {{FCSR-GAN}: Joint Face Completion and Super-resolution via Multi-task Learning},
  author  = {Jiancheng, Cai and Hu, Han and Shiguang, Shan and Xilin, chen},
  journal= {IEEE Transactions on Biometrics, Behavior, and Identity Science},
  year={2019}
}

@article{FCSRGAN_FG,
  title   = {{FCSR-GAN}: End-to-end Learning for Joint Face Completion and Super-resolution},
  author  = {Jiancheng, Cai and Hu, Han and Shiguang, Shan and Xilin, chen},
  title = {FCSR-GAN: End-to-end learning for joint face completion and super-resolution},
  booktitle = {Proc. IEEE FG},
  pages = {1--8}, 
  year = 2019
}