RGBD人脸反欺骗的交叉模态焦点损失
Cross Modal Focal Loss for RGBD Face Anti-Spoofing
摘要：
检测演示攻击的自动方法对于确保面部识别技术的可靠使用至关重要。在文献中的大多数方法呈现攻击检测（PAD）失败，在推广到看不见的攻击。近年来，已经提出了多通道方法来提高PAD系统的鲁棒性。通常，只有有限的数据量可用于附加通道，这限制了这些方法的有效性。在这项工作中，我们提出了一个新的框架PAD，使用RGB和深度通道以及一个新的损失函数。新架构使用来自两种模态的互补信息，同时减少过拟合的影响。本质上，提出了一种跨模态焦点损失函数，以将每个通道的损失贡献调制为各个通道的置信度的函数。在两个公开的数据集上的广泛评估证明了所提出的方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
