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MA-Net

Zengqun Zhao, Qingshan Liu, Shanmin Wang. "Learning Deep Global Multi-scale and Local Attention Features for Facial Expression Recognition in the Wild". IEEE Transactions on Image Processing.

Requirements

  • Python >= 3.6
  • PyTorch >= 1.2
  • torchvision >= 0.4.0

Training

  • Step 1: download basic emotions dataset of RAF-DB, and make sure it have the structure like following:
./RAF-DB/
         train/
               0/
                 train_09748.jpg
                 ...
                 train_12271.jpg
               1/
               ...
               6/
         test/
              0/
              ...
              6/

[Note] 0: Neutral; 1: Happiness; 2: Sadness; 3: Surprise; 4: Fear; 5: Disgust; 6: Anger

  • Step 2: download pre-trained model from Google Drive, and put it into ./checkpoint.

  • Step 3: change data_path in main.py to your path

  • Step 4: run python main.py

Citation

@article{zhao2021learning,
  title={Learning Deep Global Multi-scale and Local Attention Features for Facial Expression Recognition in the Wild},
  author={Zhao, Zengqun and Liu, Qingshan and Wang, Shanmin},
  journal={IEEE Transactions on Image Processing},
  volume={30},
  pages={6544-6556},
  year={2021},
  publisher={IEEE}
}

Note

The samples' number of CAER-S dataset employed in our work should be: all (69,982 samples), training set (48,995 samples), and test set (20,987 samples). We apologize for the typos in our paper.