CapsuleNet for Micro-expression Recognition

Description

This is the source code for the project joining the second Facial Micro-Expression Grand Challenge for Micro-expression Recognition Task. The challenge is organized in MEGC Workshop in conjunction with FG 2019. For more information and details, please visit the workshop website.

The log file result

result_log.csv.

Some missing and invalid clips

There are 7 clip file 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 frame out onset-offset duration

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

Requirements

• Python 3
• PyTorch
• TorchVision
• TQDM

Usage

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

python train_me_loso.py
python get_result_log.py

Project Structure

• 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.

To-do list

• Clean the code.
• Upload the pre-trained models
• Write better documentation.