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ELIM_FER

Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition (NeurIPS 2022)

PAPER | DEMO

Ubuntu PyThon PyTorch

Daeha Kim, Byung Cheol Song

CVIP Lab, Inha University

Update

  • 2022.09.20: Initialize this repository.

Requirements

  • Python (>=3.8)
  • PyTorch (>=1.7.1)
  • pretrainedmodels (>=0.7.4)
  • Wandb
  • Fabulous (terminal color toolkit)

To install all dependencies, do this.

pip install -r requirements.txt

Datasets

  1. Download four public benchmarks for training and evaluation (please download after agreement accepted).

(For more details visit website)

  1. Follow preprocessing rules for each dataset by referring pytorch official custom dataset tutorial.

Training

Just run the below script!

chmod 755 run.sh
./run.sh <method> <gpu_no> <port_no>
  • <method>: elim or elim_category
  • <gpu_no>: GPU number such as 0 (or 0, 1 etc.)
  • <port_no>: port number to clarify workers (e.g., 12345)
  • Note: If you want to try 7-class task (e.g., AffectNet), add age_script folder to your train or val. script and turn on elim_category option.

Evaluation

  • Evaluation is performed automatically at each print_check point in training phase.

Demo

  • Do to demo folder, and then feel free to use.
  • Real-time demo with pre-trained weights

TODO

  • Refactoring
  • Upload pre-trained model weights
  • Upload demo files
  • Upload train/eval files

Note

  • In case of Mlp-Mixer, please refer official repository (link)

Citation

If our work is useful for your work, then please consider citing below bibtex:

@misc{kim2022elim,
    author = {Kim, Daeha and Song, Byung Cheol},
    title = {Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition},
    Year = {2022},
    Eprint = {arXiv:2209.12172}
}

Contact

If you have any questions, feel free to contact me at kdhht5022@gmail.com.