FER SSL

Official Of our ACII 2022 paper:

Analysis of Semi-Supervised Methods for Facial Expression Recognition
Shuvendu Roy, Ali Etemad
In Proceedings of the IEEE International Conference on Affective Computing and Intelligent Interaction (ACII), 2022

Dataset

We used the following dataset

1. AffectNet
2. FER-13
3. RAF-DB

Once the dataset is downloaded use the scripts in datasets/preprocessing to preprocess the dataset. The porcessed dataset structure should look like this:

dataset
├── train
│   ├── class_001
|   |      ├── 1.jpg
|   |      ├── 2.jpg
|   |      └── ...
│   ├── class_002
|   |      ├── 1.jpg
|   |      ├── 2.jpg
|   |      └── ...
│   └── ...
└── val
    ├── class_001
    |      ├── 1.jpg
    |      ├── 2.jpg
    |      └── ...
    ├── class_002
    |      ├── 1.jpg
    |      ├── 2.jpg
    |      └── ...
    └── ...

Run

Modify the config files in config/ directory if needed.

python [ALGO_NAME].py --c [CONFIG_FILE]

Results

Acknowledgements

The semi-supervised algorithm implementations are followed from the following repository: TorchSSL. We thank the authors for releasing their code. If you use our model and code, please consider citing these works as well.

Citation

Please cite our paper if you this code repo in your work.

@inproceedings{roy2022analysis,
  title={Analysis of Semi-Supervised Methods for Facial Expression Recognition},
  author={Roy, Shuvendu and Etemad, Ali},
  booktitle={2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)},
  pages={1--8},
  year={2022},
  organization={IEEE}
}

Contact

Thanks for your attention! If you have any suggestion or question, you can leave a message here or contact us directly:

• shuvendu.roy@queensu.ca