论文标题
造型脸部变形并改善脸部变形攻击探测器
Style Your Face Morph and Improve Your Face Morphing Attack Detector
论文作者
论文摘要
变形的面部图像是一个合成创建的图像，看起来与两个受试者的面非常相似，两者都可以使用它来对其进行生物识别验证系统进行验证。它可以通过对齐和混合两个主题的面部图像来轻松创建。在本文中，我们提出了一种基于样式转移的方法，可提高变形面图像的质量。在创建由混合引起的变形面部图像过程中，它反驳了图像变性。我们分析了艺术的不同状态面对变形攻击检测系统，以针对我们改进的变形面图像和其他改善图像质量的方法。当首先面对我们改进的变形面图像时，所有检测系统的性能都明显更糟。可以通过将我们的质量改进的形态添加到培训数据中来增强其中的大多数，从而进一步提高了与其他质量提高手段的鲁棒性。
A morphed face image is a synthetically created image that looks so similar to the faces of two subjects that both can use it for verification against a biometric verification system. It can be easily created by aligning and blending face images of the two subjects. In this paper, we propose a style transfer based method that improves the quality of morphed face images. It counters the image degeneration during the creation of morphed face images caused by blending. We analyze different state of the art face morphing attack detection systems regarding their performance against our improved morphed face images and other methods that improve the image quality. All detection systems perform significantly worse, when first confronted with our improved morphed face images. Most of them can be enhanced by adding our quality improved morphs to the training data, which further improves the robustness against other means of quality improvement.
