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. |
| Keywords: | |
| Projects: |
Idiap ODIN/BATL |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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