README

Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition

This is the offical code for SLSTT. See our paper on [IEEE] and [arXiv]

The whole code is not available now, but you can use

python LOSO.py -e

to test the exisitng example models, which could be download from huggingface.

Before training

You can see data directory <preprocess/databases>.

Please put the raw data in <preprocess/databases/DATABASE/data>, for example:

<preprocess/databases/casme2/data>.

After putting the raw data in the correct directory, you can simplely run

python preprocess/data_init.py 

to generate all long-term optical flow input images from SMIC, CASME II and SAMM databases.

Training

You can simply use:

python trainer.py -d DATABASE -s S

or

python LOSO.py -d DATABASE

to run slstt for DATABASE ("smic", "casme2", "samm" or "com-DATABASE") and left subject S for validation, for further training(there is also alternative option) please carefully read the code.

Evaluate

python main.py -d DATABASE -s S --modal-path XXX -e

Fill the XXX with your checkpoint path with leave subject S out.

Also you can evaluate with LOSO start from subjuect S, (for examle, samm should start from subject 6).

python LOSO.py -d DATABASE --start-sub S  --dir FOLDER -e

Please ensure all your models for DATABASE ("com" for composite dataset of CDE) are in FOLDER.

Correction of Confusion Matrix

On IEEE version, we mis-swapped the predictions and targets when generate visualised confusion matrix images. Sorry for any confusion this may have caused. We have corrected our arXiv version and show the correct one here.

Citiation

If you find our paper and this code useful in your research, please consider citing:

@article{zhang2022short,
  title={Short and long range relation based spatio-temporal transformer for micro-expression recognition},
  author={Zhang, Liangfei and Hong, Xiaopeng and Arandjelovi{\'c}, Ognjen and Zhao, Guoying},
  journal={IEEE Transactions on Affective Computing},
  year={2022},
  publisher={IEEE},
  pages={1-13},
  doi={10.1109/TAFFC.2022.3213509}
  }