%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{George_CVPR_2021,
         author = {George, Anjith and Marcel, S{\'{e}}bastien},
       projects = {Idiap, ODIN/BATL},
          title = {Cross Modal Focal Loss for RGBD Face Anti-Spoofing},
      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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2021/George_CVPR_2021.pdf}
}