We provide several publicly available lightweight models for face recognition and facial expression recognition that obtained high accuracy of LFW (Labeled Faces in-the-Wild) and AffectNet datasets.
The demo Android application is available at our Google drive. Here is the result of its running on my personal device:
If you use our models, please cite the following papers:
@incollection{savchenko2024device,
title={Device-Specific Facial Descriptors: Winning a Lottery with a SuperNet},
author={Savchenko, Andrey and Maslov, Dmitry and Makarov, Ilya},
booktitle={ECAI},
pages={4439--4442},
year={2024},
publisher={IOS Press}
}
@article{savchenko2024autoface,
author={Savchenko, Andrey V.},
journal={IEEE Access},
title={{AutoFace}: How to Obtain Mobile Neural Network-Based Facial Feature Extractor in Less Than 10 Minutes?},
year={2024},
volume={12},
pages={25106-25118},
doi={10.1109/ACCESS.2024.3365928}}
@article{savchenko2023fast,
author={Savchenko, Andrey V. and Savchenko, Lyudmila V. and Makarov, Ilya},
journal={IEEE Access},
title={Fast Search of Face Recognition Model for a Mobile Device Based on Neural Architecture Comparator},
year={2023},
volume={11},
pages={65977-65990},
doi={10.1109/ACCESS.2023.3290902}
}