Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing
Abstract
This study reveals a cutting-edge re-balanced contrastive learning strategy aimed at strengthening face anti-spoofing capabilities within facial recognition systems, with a focus on countering the challenges posed by printed photos, and highly realistic silicone or latex masks. Leveraging the HySpeFAS dataset, which benefits from Snapshot Spectral Imaging technology to provide hyperspectral images, our approach harmonizes class-level contrastive learning with data resampling and an innovative real-face oriented reweighting technique. This method effectively mitigates dataset imbalances and reduces identity-related biases. Notably, our strategy achieved an unprecedented 0.0000% Average Classification Error Rate (ACER) on the HySpeFAS dataset, ranking first at the Chalearn Snapshot Spectral Imaging Face Anti-spoofing Challenge on CVPR 2024.
Cite
Text
Song et al. "Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00103Markdown
[Song et al. "Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/song2024cvprw-supervised/) doi:10.1109/CVPRW63382.2024.00103BibTeX
@inproceedings{song2024cvprw-supervised,
title = {{Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing}},
author = {Song, Chuanbiao and Hong, Yan and Lan, Jun and Zhu, Huijia and Wang, Weiqiang and Zhang, Jianfu},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {980-985},
doi = {10.1109/CVPRW63382.2024.00103},
url = {https://mlanthology.org/cvprw/2024/song2024cvprw-supervised/}
}