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ial-for-facial-expression-recognition's Introduction

IAL-for-Facial-Expression-Recognition

Dynamic Facial Expression Recognition

main.py -- contains the code of the proposed intensity-aware loss

modules.py -- contains the code of the proposed global convolution-attention block

video_transform.py \& dataset_DFEW.py -- contain the code of the data augmentation and the dynamic sampling strategy used in our methods

final_model.py -- is just used to build the overall model based on ''modules.py''

Training on DFEW dataset

# begin training and evaluation
python main.py

Citation

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

@inproceedings{li2022intensity,
  title={Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild},
  author={Li, Hanting and Niu, Hongjing and Zhu, Zhaoqing and Zhao, Feng},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  year={2023}
}

ial-for-facial-expression-recognition's People

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ial-for-facial-expression-recognition's Issues

Hyperparameters on ferv39k

I am very interested in your work! I would like to know if there are any other parameters that need to be adjusted in FERV39K, besides the difference in sampling from DFEW.

I used your method a few days ago, but I only got 43% of WAR on FERV39K, so I would like to know if there are any other parameters that need to be adjusted. Many thanks!

FERV39k database

Hi, thanks a lot for your help, can you please provide the code related to FERV39k dataset? Thank you so much.

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