This is the source code for the paper CapsuleNet for Micro-expression Recognition joining the second Facial Micro-Expression Grand Challenge for Micro-expression Recognition Task. If you find this code useful, please kindly cite the our paper as follows:

# Bibtex
@INPROCEEDINGS{Quang2019Capsulenet,
  author={N. V. {Quang} and J. {Chun} and T. {Tokuyama}},
  booktitle={2019 14th IEEE International Conference on Automatic Face   Gesture Recognition (FG 2019)}, 
  title={CapsuleNet for Micro-Expression Recognition}, 
  year={2019},
  volume={},
  number={},
  pages={1-7},}




# Plain text
N. V. Quang, J. Chun and T. Tokuyama, "CapsuleNet for Micro-Expression Recognition," 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France, 2019, pp. 1-7, doi: 10.1109/FG.2019.8756544.

result_log.csv.

# the reproduced log file result
> `result_log_reproduced.csv`.

There are 7 clip files which are missing or invalid.

• The below clips don't exist in the downloaded datasets

smic/HS_long/SMIC_HS_E/s03/s3_ne_03 not exists
smic/HS_long/SMIC_HS_E/s03/s3_ne_20 not exists
smic/HS_long/SMIC_HS_E/s04/s4_ne_05 not exists
smic/HS_long/SMIC_HS_E/s04/s4_ne_06 not exists
smic/HS_long/SMIC_HS_E/s09/s9_sur_02 not exists

• The invalid clips in which the apex frames are out of onset-offset duration

samm/28/028_4_1
samm/32/032_3_1

• Python 3
• PyTorch
• TorchVision
• TQDM

Run the following script to reproduce the result in the log file.

python train_me_loso.py
python get_result_log.py

• smic_processing.py: Preprocess the SMIC dataset: detect the apex frames.
• train_me_loso.py: Perform LOSO cross-validation on our proposed model.
• train_me_loso_baseline.py: Perform LOSO cross-validation on baseline models (ResNet18, VGG11).
• get_result_log.py: Write the result log file from the pickle file.
• capsule: The package for building CapsuleNet and Loss.