ekman_expressions

This repository contains all the models trained in the scientific article titled "Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI".

To reproduce the results of the article, please refer to the Github project containing all code used: https://github.com/Xavi3398/ekman_expressions.

Datasets used for training:

The datasets used were 5: CK+, BU-4DFE, JAFFE and WSEFEP, and FEGA. These are some examples from each class:

Models:

We explored 12 different Deep Learning models:

Model	Image Size	Pre-training	Parameters
AlexNet	224x224	No	88.7 M
WeiNet	64x64	No	1.7 M
SongNet	224x224	No	172.7 K
SilNet	150x150	No	184.9 M
VGG16	224x224	Yes	14.7 M
VGG19	224x224	Yes	20 M
ResNet50	224x224	Yes	23.6 M
ResNet101V2	224x224	Yes	42.6 M
InceptionV3	224x224	Yes	21.8 M
Xception	224x224	Yes	20.9 M
MobileNetV3	224x224	Yes	3 M
EfficientNetV2	224x224	Yes	5.9 M

License

This project is licensed under the terms of the MIT license. See the LICENSE file for details.

Acknowledgments

Grant PID2019-104829RA-I00 funded by MCIN/ AEI /10.13039/501100011033. Project EXPLainable Artificial INtelligence systems for health and well-beING (EXPLAINING)

This work is part of the Project PID2022-136779OB-C32 (PLEISAR) funded by MICIU/ AEI /10.13039/501100011033/ and FEDER, EU. Project Playful Experiences with Interactive Social Agents and Robots (PLEISAR): Social Learning and Intergenerational Communication.

F. X. Gaya-Morey was supported by an FPU scholarship from the Ministry of European Funds, University and Culture of the Government of the Balearic Islands.

Citation

If you use this code in your research, please cite our paper:

@misc{gayamorey2024unveilinghumanlikesimilaritiesautomatic,
      title={Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI}, 
      author={F. Xavier Gaya-Morey and Silvia Ramis-Guarinos and Cristina Manresa-Yee and Jose M. Buades-Rubio},
      year={2024},
      eprint={2401.11835},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2401.11835}, 
}

Contact

If you have any questions or feedback, please feel free to contact the authors.