3D Face Anti-Spoofing With Factorized Bilinear Coding
3D Face Anti-Spoofing With Factorized Bilinear Coding
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DOI：
10.1109/tcsvt.2020.3044986
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发表时间：
2020-05
影响因子：
8.4
通讯作者：
Shan Jia;Xin Li;Chuanbo Hu;G. Guo;Zhengquan Xu
中科院分区：
文献类型：
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作者：
Shan Jia;Xin Li;Chuanbo Hu;G. Guo;Zhengquan Xu
近年来，我们见证了人脸呈现攻击模型和呈现攻击检测（PAD）的快速发展。与广泛研究的2D人脸呈现攻击相比，3D人脸欺骗攻击更具挑战性，因为人脸识别系统更容易被与真实的人脸相似的材料的3D特性所迷惑。在这项工作中，我们解决了检测这些真实的3D人脸呈现攻击的问题，并提出了一种新的反欺骗方法从细粒度分类的角度。我们的方法，基于多个颜色通道的分解双线性编码（即MC_FBC），目标是学习真实的和假图像之间的细微差别。通过从RGB和YCbCr空间中提取鉴别信息并融合互补信息，我们开发了一种原理性的3D人脸欺骗检测解决方案。一个大规模的蜡像人脸数据库（WFFD）的图像和视频也被收集作为超现实攻击，以促进3D人脸呈现攻击检测的研究。大量的实验结果表明，我们提出的方法实现了国家的最先进的性能在我们自己的WFFD和其他人脸欺骗数据库下的各种数据库内和数据库间的测试方案。
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.