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.