SFAMNet: A Scene Flow Attention-based Micro-expression Network
Neurocomputing | Paper | Bibtex |
(Released on April, 2023)
Results
Performance comparison for micro-expression spotting.
Performance comparison for micro-expression recognition.
Performance comparison for micro-expression analysis.
How to run the code
Step 1) Download the processed_data from:
hidden at the moment
The files are structured as follows:
+-annotation
+-pretrained_weights
+-Utils
+-dataloader.py
+-load_data.py
+-main.py
+-network.py
+-prepare_data.py
+-requirements.txt
+-train.py
+-train_utils.py
+-processed_data+-CASME_cube_recog_rgbd-flow.pkl
+-CASME_cube_spot_rgbd-flow.pkl
Step 2) Installation of packages using pip
pip install -r requirements.txt
Step 3) Network Training and Evaluation
python main.py
Note for parameter settings
--train (True/False)
--emotion (4/7)
Citation
If you find this work useful for your research, please cite
title={SFAMNet: A scene flow attention-based micro-expression network},
author={Liong, Gen-Bing and Liong, Sze-Teng and Chan, Chee Seng and See, John},
journal={Neurocomputing},
volume={566},
pages={126998},
year={2024},
publisher={Elsevier}
}
Feedback
Suggestions and opinions on this work (both positive and negative) are greatly welcomed. Please contact the authors by sending an email to
genbing67@gmail.com or cs.chan at um.edu.my.
License and Copyright
The project is open source under BSD-3 license (see the LICENSE file).
(c)2023 Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, Universiti Malaya.