Analysis of Semi-Supervised Methods for Facial Expression Recognition

Official Code for Analysis of Semi-Supervised Methods for Facial Expression Recognition. The paper has been accepted in Affective Computing and Intelligent Interaction (ACII), 2022.

drawing

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

drawing

Acknowledgements

The semi-supervised algorithm implementations are followed from the following repository: TorchSSL

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}
}

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