Zengqun Zhao, Qingshan Liu, Feng Zhou. "Robust Lightweight Facial Expression Recognition Network with Label Distribution Training". AAAI'21
- Python >= 3.6
- PyTorch >= 1.2
- torchvision >= 0.4.0
- Step 1: download basic emotions dataset of RAF-DB, and make sure it has the structure like the 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 the --data in run.sh to your path
- Step 4: run
sh run.sh
- Add test and visualization code. (May. 5, 2023 Update)
- Sept. 16, 2021 Update
We provide the pre-trained ResNet-18 and ResNet-50 on MS-Celeb-1M (classes number is 12666) for your research.
The Google Driver for ResNet-18 model. The Google Driver for ResNet-50 model.
The pre-trained ResNet-50 model can be also used for LDG. - Nov. 6, 2021 Update
The fine-tuned LDG models on CAER-S, AffectNet-7, and AffectNet-8 can be downloaded here, here, and here, respectively. - Nov. 12, 2021 Update
The trained EfficientFace model on RAF-DB, CAER-S, AffectNet-7, and AffectNet-8 can be downloaded here, here, here, and here, respectively. As demonstrated in the paper, the testing accuracy is 88.36%, 85.87%, 63.70%, and 59.89%, respectively.
@inproceedings{zhao2021robust,
title={Robust Lightweight Facial Expression Recognition Network with Label Distribution Training},
author={Zhao, Zengqun and Liu, Qingshan and Zhou, Feng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={4},
pages={3510--3519},
year={2021}
}The samples' number of the 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.