论文标题
您的面部探测器可以进行反欺骗吗？通过多通道探测器的面部表现攻击检测检测
Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector
论文作者
论文摘要
在典型的面部识别管道中，面部探测器的任务是定位面部区域。但是，面部探测器将看起来像脸部的区域定位，而不论面部的活力如何，这使整个系统都容易受到演示攻击。在这项工作中，我们试图重新制定面部探测器检测真实面孔的任务，从而消除了演示攻击的威胁。尽管仅使用可见的光谱图像可能会具有挑战性，但我们利用了从架子设备（例如颜色，深度和红外频道）提供的多渠道信息来设计多通道面部探测器。所提出的系统可以用作实时检测器，以消除对单独的演示攻击检测模块的需求，从而使该系统在实践中可靠，而无需任何其他计算开销。主要想法是利用单阶段对象检测框架，并从不同的通道中获得PAD任务的联合表示。我们已经评估了我们的多频道WMCA数据集中的方法，该数据集包含各种攻击，以显示提出的框架的有效性。
In a typical face recognition pipeline, the task of the face detector is to localize the face region. However, the face detector localizes regions that look like a face, irrespective of the liveliness of the face, which makes the entire system susceptible to presentation attacks. In this work, we try to reformulate the task of the face detector to detect real faces, thus eliminating the threat of presentation attacks. While this task could be challenging with visible spectrum images alone, we leverage the multi-channel information available from off the shelf devices (such as color, depth, and infrared channels) to design a multi-channel face detector. The proposed system can be used as a live-face detector obviating the need for a separate presentation attack detection module, making the system reliable in practice without any additional computational overhead. The main idea is to leverage a single-stage object detection framework, with a joint representation obtained from different channels for the PAD task. We have evaluated our approach in the multi-channel WMCA dataset containing a wide variety of attacks to show the effectiveness of the proposed framework.
