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 [BibTeX] [Marc21]
Cross Modal Focal Loss for RGBD Face Anti-Spoofing
Type of publication: Conference paper
Citation: George_CVPR_2021
Publication status: Accepted
Booktitle: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021
Abstract: Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the litera- ture for presentation attack detection (PAD) fails in gen- eralizing to unseen attacks. In recent years, multi-channel methods have been proposed to improve the robustness of PAD systems. Often, only a limited amount of data is avail- able for additional channels, which limits the effectiveness of these methods. In this work, we present a new framework for PAD that uses RGB and depth channels together with a novel loss function. The new architecture uses complemen- tary information from the two modalities while reducing the impact of overfitting. Essentially, a cross-modal focal loss function is proposed to modulate the loss contribution of each channel as a function of the confidence of individual channels. Extensive evaluations in two publicly available datasets demonstrate the effectiveness of the proposed ap- proach.
Projects Idiap
Authors George, Anjith
Marcel, S├ębastien
Added by: [UNK]
Total mark: 0
  • George_CVPR_2021.pdf
       (Accepted for Publication in CVPR2021)