The vulnerability of face recognition systems to different presentation attacks has aroused increasing concern in the biometric community. Face presentation detection (PAD) techniques, which aim to distinguish real face samples from spoof artifacts, are the efficient countermeasure. In recent years, various methods have been proposed to address 2D type face presentation attacks, including photo print attack and video replay attack. However, it is difficult to tell which methods perform better for these attacks, especially in practical mobile authentication scenarios, since there is no systematic evaluation or benchmark of the state-of-the-art methods on a common ground (i.e., using the same databases and protocols). Therefore, this paper presents a comprehensive evaluation of several representative face PAD methods (30 in total) on three public mobile spoofing datasets to quantitatively compare the detection performance. Furthermore, the generalization ability of existing methods is tested under cross-database testing scenarios to show the possible database bias. We also summarize meaningful observations and give some insights that will help promote both academic research and practical applications.

人脸识别系统易受不同的呈现攻击之害，引起了生物识别界越来越多的关注。有效的对策是旨在区分真实面部样本与欺骗伪像的面部表情检测（PAD）技术。近年来，已经提出了各种方法来应对2D类型的面部呈现攻击，包括照片打印攻击和视频重放攻击。但是，很难确定哪种方法对这些攻击的性能更好，尤其是在实际的移动身份验证场景中，因为没有基于共同点的最新技术方法的系统评估或基准测试（即，使用相同的方法）数据库和协议）。因此，本文针对三个公共移动欺骗数据集，对几种代表性的面部PAD方法（总共30种）进行了全面评估，以定量比较检测性能。此外，在跨数据库测试方案下测试了现有方法的泛化能力，以显示可能的数据库偏差。我们还总结了有意义的观察，并提供了一些见识，将有助于促进学术研究和实际应用。