With the rapid development in face recognition, most of the existing systems can perform very well in unconstrained scenarios. However, it is still a very challenging task to detect face spoofing attacks, thus face anti-spoofing has become one of the most important research topics in the community. Though various anti-spoofing models have been proposed, the generalisation capability of these models usually degrades for unseen attacks in the presence of challenging appearance variations, e.g. , background, illumination, diverse spoofing materials and low image quality. To address this issue, we propose to use a Generative Adversarial Network (GAN) that transfers an input face image from the RGB domain to the depth domain. The generated depth clue enables biometric preservation against challenging appearance variations and diverse image qualities. To be more specific, the proposed method has two main stages. The first one is a GAN-based domain transfer module that converts an input image to its corresponding depth map. By design, a live face image should be transferred to a depth map whereas a spoofing face image should be transferred to a plain (black) image. The aim is to improve the discriminative capability of the proposed system. The second stage is a classification model that determines whether an input face image is live or spoofing. Benefit from the use of the GAN-based domain transfer module, the latent variables can effectively represent the depth information, complementarily enhancing the discrimination of the original RGB features. The experimental results obtained on several benchmarking datasets demonstrate the effectiveness of the proposed method, with superior performance over the state-of-the-art methods. The source code of the proposed method is publicly available at https://github.com/coderwangson/DFA .

中文翻译：

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从 RGB 到深度：用于人脸反欺骗的域传输网络

随着人脸识别技术的飞速发展，现有的大部分系统都可以在无约束的场景下表现得很好。然而，检测人脸欺骗攻击仍然是一项非常具有挑战性的任务，因此人脸反欺骗已成为社区最重要的研究课题之一。尽管已经提出了各种反欺骗模型，但这些模型的泛化能力通常会在存在具有挑战性的外观变化的情况下因看不见的攻击而降低，例如，背景、照明、各种欺骗材料和低图像质量。为了解决这个问题，我们建议使用生成对抗网络 (GAN) 将输入人脸图像从 RGB 域传输到深度域。生成的深度线索能够针对具有挑战性的外观变化和不同的图像质量进行生物识别保存。更具体地说，所提出的方法有两个主要阶段。第一个是基于 GAN 的域传输模块，它将输入图像转换为其相应的深度图。根据设计，实时人脸图像应转换为深度图，而欺骗人脸图像应转换为普通（黑色）图像。目的是提高所提出系统的判别能力。第二阶段是一个分类模型，它确定输入的人脸图像是真实的还是欺骗的。受益于使用基于 GAN 的域传输模块，潜在变量可以有效地表示深度信息，互补地增强了对原始 RGB 特征的辨别力。在几个基准数据集上获得的实验结果证明了所提出方法的有效性，并且性能优于最先进的方法。所提出方法的源代码可在以下网址公开获得 在几个基准数据集上获得的实验结果证明了所提出方法的有效性，并且性能优于最先进的方法。所提出方法的源代码可在以下网址公开获得 在几个基准数据集上获得的实验结果证明了所提出方法的有效性，并且性能优于最先进的方法。所提出方法的源代码可在以下网址公开获得https://github.com/coderwangson/DFA .