From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos
Yin Chen$^{†}$, Jia Li$^{†∗}$, Shiguang Shan, Meng Wang, and Richang Hong

[2024.9.5] The fine-tuned checkpoints are available.

[2024.9.2] The code and pre-trained models are available.

[2024.8.28] The paper is accepted by IEEE Transactions on Affective Computing.

[2023.12.5] Code and pre-trained models will be released here.

1、 Download the pre-trained weights from baidu drive or google drive, and move it to the ckpts directory.

2、 Run the following command to fine-tune the model on the target dataset.

conda create -n s2d python=3.9
conda activate s2d
pip install -r requirements.txt
bash run.sh

The fine-tuned checkpoints can be downloaded from here.

If you find this work helpful, please consider citing:

@ARTICLE{10663980,
  author={Chen, Yin and Li, Jia and Shan, Shiguang and Wang, Meng and Hong, Richang},
  journal={IEEE Transactions on Affective Computing}, 
  title={From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos}, 
  year={2024},
  volume={},
  number={},
  pages={1-15},
  keywords={Adaptation models;Videos;Computational modeling;Feature extraction;Transformers;Task analysis;Face recognition;Dynamic facial expression recognition;emotion ambiguity;model adaptation;transfer learning},
  doi={10.1109/TAFFC.2024.3453443}}