Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing 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 available 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 complementary 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 approach.

本研究提出了一种采用 RGB 和深度通道的新框架，结合一种新的损失函数用于检测人脸识别中的攻击，通过多通道提取信息，减小过拟合的影响并提高鲁棒性。新的跨模态焦点损失函数通过基于信道的置信度来调制每个信道的损失贡献，通过两个公开的数据集进行广泛评估，证明了该方法的有效性。

RGBD人脸反欺诈的跨模态焦点损失