jiangxingxun/former-dfer

“Former-DFER: Dynamic Facial Expression Recognition Transformer”, ACM MM 2021.

Former-DFER

Zengqun Zhao, Qingshan Liu. "Former-DFER: Dynamic Facial Expression Recognition Transformer". ACM International Conference on Multimedia.

Setup

conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=10.2 -c pytorch

Training on DFEW

• Step 1: download DFEW dataset.
• Step 2: fill in all the your_dataset_path in script.py, then run script.py.
• Step 3: run sh main_DFEW_trainer.sh

Recent Updates

Pretrain Models on DFEW

The trained models on DFER (fd1, fd2, fd3, fd4, fd5) can be downloaded here (Google Driver).

Performance on FERV39k

Recently, a new dynamic FER dataset named FERV39k is proposed, the results of the Former-DFER on FERV39k are as follows:

Happiness	Sadness	Neutral	Anger	Surprise	Disgust	Fear	UAR	WAR
67.57	44.16	51.81	48.93	25.09	10.80	9.80	36.88	45.72

Citation

If you find our work useful, please consider citing our paper:

@inproceedings{zhao2021former,
  title={Former-DFER: Dynamic Facial Expression Recognition Transformer},
  author={Zhao, Zengqun and Liu, Qingshan},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={1553--1561},
  year={2021}
}