Abstract
Face presentation attacks have become a major threat to face recognition systems and many countermeasures have been proposed in the past decade. However, most of them are devoted to 2D face presentation attacks, 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. So, we propose a novel detection method for 3D face mask presentation attack by modeling reflectance differences based on intrinsic image analysis. In the proposed method, the face image is first processed with intrinsic image decomposition 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, the 1D convolutional network is further used to capture the information for describing different materials or surfaces react differently to changes in illumination. Extensive experiments on the 3DMAD database demonstrate the effectiveness of our proposed method in distinguishing a face mask from the real one and show that the detection performance outperforms other state-of-the-art methods.
Abstract (translated)
人脸呈现攻击已成为人脸识别系统的主要威胁，近十年来，人们提出了许多对策。然而，它们中的大多数都是用于二维人脸呈现攻击，而不是三维人脸遮罩。与真实面膜不同，3D面膜通常由树脂材料制成，表面光滑，从而产生反射差异。因此，我们提出了一种基于内禀图像分析的反射差建模方法来检测三维人脸掩模的呈现攻击。该方法首先对人脸图像进行固有图像分解，计算其反射率图像。然后，从三个正交平面中提取强度分布柱状图，以表示真实人脸和三维人脸遮罩之间反射图像的强度差。然后，进一步利用一维卷积网络捕获描述不同材料或表面的信息，对光照变化做出不同的反应。在3DMAD数据库上进行了大量的实验，证明了我们提出的方法在区分人脸面具和真实人脸面具方面的有效性，并表明检测性能优于其他最先进的方法。
URL
https://arxiv.org/abs/1903.11303
