The source code was trying to reproduce the paper - "Micro-expression Recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion". [paper] [official code]
# Install requirement
$ pip install -r requirements.txt
# Download landmarks weight for DLIB
$ mkdir -p dataloader/weight
$ wget https://github.com/davisking/dlib-models/raw/master/mmod_human_face_detector.dat.bz2 -P dataloader/weight
$ bzip2 -d dataloader/weight/mmod_human_face_detector.dat.bz2
$ wget https://github.com/davisking/dlib-models/raw/master/shape_predictor_68_face_landmarks.dat.bz2 -P dataloader/weight
$ bzip2 -d dataloader/weight/shape_predictor_68_face_landmarks.dat.bz2The structure of MagNet was adapted from here. Please download the pretrained weight from their release and place in dataloader/weight/.
# Remove the cpu version first
$ pip uninstall dlib
# Install cudnn and its toolkit
$ conda install cudnn cudatoolkit
# Build from source
$ git clone https://github.com/davisking/dlib.git
$ cd dlib
$ mkdir build & cd build
$ cmake .. \
-DDLIB_USE_CUDA=1 \
-DUSE_AVX_INSTRUCTIONS=1 \
-DCMAKE_PREFIX_PATH=<path to conda env>\
-DCMAKE_C_COMPILER=gcc-6 -DCMAKE_CXX_COMPILER=g++-6
$ cmake --build .
$ cd ..
$ python setup.py install \
--set USE_AVX_INSTRUCTIONS=1 \
--set DLIB_USE_CUDA=1 \
--set CMAKE_PREFIX_PATH=<path to conda env> \
--set CMAKE_C_COMPILER=gcc-6 \
--set CMAKE_CXX_COMPILER=g++-usage: train.py [-h] --csv_path CSV_PATH --image_root IMAGE_ROOT --npz_file
NPZ_FILE --catego CATEGO [--num_classes NUM_CLASSES]
[--batch_size BATCH_SIZE]
[--weight_save_path WEIGHT_SAVE_PATH] [--epochs EPOCHS]
[--learning_rate LEARNING_RATE]
optional arguments:
-h, --help show this help message and exit
--csv_path CSV_PATH Path for the csv file for training data
--image_root IMAGE_ROOT
Root for the training images
--npz_file NPZ_FILE Files root for npz
--catego CATEGO SAMM or CASME dataset
--num_classes NUM_CLASSES
Classes to be trained
--batch_size BATCH_SIZE
Training batch size
--weight_save_path WEIGHT_SAVE_PATH
Path for the saving weight
--epochs EPOCHS Epochs for training the model
--learning_rate LEARNING_RATE
Learning rate for training the model
@InProceedings{Lei_2021_CVPR,
author = {Lei, Ling and Chen, Tong and Li, Shigang and Li, Jianfeng},
title = {Micro-Expression Recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021},
pages = {1571-1580}
}