We have witnessed rapid advances in both face presentation attack models and
presentation attack detection (PAD) in recent years. When compared with widely
studied 2D face presentation attacks, 3D face spoofing attacks are more
challenging because face recognition systems are more easily confused by the 3D
characteristics of materials similar to real faces. In this work, we tackle the
problem of detecting these realistic 3D face presentation attacks, and propose
a novel anti-spoofing method from the perspective of fine-grained
classification. Our method, based on factorized bilinear coding of multiple
color channels (namely MC\_FBC), targets at learning subtle fine-grained
differences between real and fake images. By extracting discriminative and
fusing complementary information from RGB and YCbCr spaces, we have developed a
principled solution to 3D face spoofing detection. A large-scale wax figure
face database (WFFD) with both images and videos has also been collected as
super-realistic attacks to facilitate the study of 3D face presentation attack
detection. Extensive experimental results show that our proposed method
achieves the state-of-the-art performance on both our own WFFD and other face
spoofing databases under various intra-database and inter-database testing
scenarios.

本文提出一种新的对抗超真实 3D 人脸攻击的方法，基于细粒度分类，使用多个颜色通道的因式双线性编码并提取 RGB 和 YCbCr 空间的鉴别性和互补信息，同时收集了大规模蜡像人脸数据库进行实验验证并取得了最先进的性能。