Face presentation attacks have become a major threat against face recognition systems and many countermeasures have been proposed over the past decade. However, most of them are devoted to 2D face presentation attack detection, rather than 3D face masks. Unlike the real face, the 3D face mask is usually made of resin materials and has a smooth surface, resulting in reflectance differences. Therefore, in this study, the authors propose a novel 3D face mask presentation attack detection method based on analysis of image reflectance. In the proposed method, the face image is first processed with intrinsic image decomposition algorithm to compute its reflectance image. Then, the intensity distribution histograms are extracted from three orthogonal planes to represent the intensity differences of reflectance images between the real face and 3D face mask. After that, given that the reflectance image of a smooth surface is more sensitive to illumination changes, 1D convolutional neural network is used to characterise how different materials or surfaces react differently to illumination changes. Extensive experiments with the public available 3DMAD database demonstrate the effectiveness of the proposed method for distinguishing a face mask from the real one and show that the detection performance outperforms other state-of-the-art methods.

中文翻译：

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基于内在图像分析的3D面罩演示攻击检测

面部表情攻击已经成为对面部识别系统的主要威胁，并且在过去的十年中提出了许多对策。然而，它们中的大多数致力于2D人脸呈现攻击检测，而不是3D面罩。与真实面部不同，3D面罩通常由树脂材料制成并具有光滑的表面，从而导致反射率差异。因此，在这项研究中，作者提出了一种基于图像反射率分析的新型3D面罩呈现攻击检测方法。在提出的方法中，首先使用固有图像分解算法处理面部图像以计算其反射率图像。然后，从三个正交平面提取强度分布直方图，以表示真实面部和3D面罩之间的反射图像的强度差异。此后，假设光滑表面的反射率图像对照明变化更为敏感，则使用一维卷积神经网络来表征不同的材料或表面对照明变化的反应不同。使用公开的3DMAD数据库进行的大量实验证明了所提出的区分口罩与真实口罩的方法的有效性，并表明检测性能优于其他最新方法。一维卷积神经网络用于表征不同的材料或表面对照明变化的不同反应。使用公开的3DMAD数据库进行的大量实验证明了所提出的区分口罩与真实口罩的方法的有效性，并表明检测性能优于其他最新方法。一维卷积神经网络用于表征不同的材料或表面对照明变化的不同反应。使用公开的3DMAD数据库进行的大量实验证明了所提出的区分口罩与真实口罩的方法的有效性，并表明检测性能优于其他最新方法。