论文标题
面部启动：从单个变形中提取组件面
Facial De-morphing: Extracting Component Faces from a Single Morph
论文作者
论文摘要
面部变体是通过战略性地结合了与多个身份相对应的两个或多个面部图像来创建的。目的是使变形图像与多个身份匹配。当前的变形攻击检测策略可以检测到变体，但无法恢复创建它们的图像或身份。从变形的面部图像中推论单个面部图像的任务称为\ textit {dempherphing}。截图的现有工作假定与一个身份有关的参考图像的可用性，以恢复同伙的图像 - 即其他身份。在这项工作中，我们提出了一种新颖的截形方法，该方法可以从单个变形的面部图像中同时恢复两种身份的图像，而无需参考图像或有关变形过程的先前信息。我们提出了一个生成的对抗网络，该网络以惊人的高度视觉现实主义和与原始脸部图像相似的高度高度视觉现实主义和生物识别相似性，实现了单一基于图像的截面。我们证明了我们的方法在基于里程碑的形态和基于生成模型的形态上的性能，并具有令人鼓舞的结果。
A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple identities. Current morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. The task of deducing the individual face images from a morphed face image is known as \textit{de-morphing}. Existing work in de-morphing assume the availability of a reference image pertaining to one identity in order to recover the image of the accomplice - i.e., the other identity. In this work, we propose a novel de-morphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process. We propose a generative adversarial network that achieves single image-based de-morphing with a surprisingly high degree of visual realism and biometric similarity with the original face images. We demonstrate the performance of our method on landmark-based morphs and generative model-based morphs with promising results.
