Face recognition is a mainstream biometric authentication method. However, the vulnerability to presentation attacks (a.k.a. spoofing) limits its usability in unsupervised applications. Even though there are many methods available for tackling presentation attacks (PA), most of them fail to detect sophisticated attacks such as silicone masks. As the quality of presentation attack instruments improves over time, achieving reliable PA detection with visual spectra alone remains very challenging. We argue that analysis in multiple channels might help to address this issue. In this context, we propose a multi-channel Convolutional Neural Network-based approach for presentation attack detection (PAD). We also introduce the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks. Data from different channels such as color, depth, near-infrared, and thermal are available to advance the research in face PAD. The proposed method was compared with feature-based approaches and found to outperform the baselines achieving an ACER of 0.3% on the introduced dataset. The database and the software to reproduce the results are made available publicly.

中文翻译：

---

使用多通道卷积神经网络进行生物识别人脸呈现攻击检测


人脸识别是主流的生物识别认证方法。然而，演示攻击（又称欺骗）的漏洞限制了其在无人监督应用程序中的可用性。尽管有许多方法可用于应对演示攻击 (PA)，但大多数方法都无法检测复杂的攻击，例如硅胶掩模。随着演示攻击工具的质量随着时间的推移而提高，仅通过视觉光谱实现可靠的 PA 检测仍然非常具有挑战性。我们认为，多渠道分析可能有助于解决这个问题。在这种情况下，我们提出了一种基于多通道卷积神经网络的演示攻击检测（PAD）方法。我们还引入了用于面部 PAD 的新宽多通道演示攻击 (WMCA) 数据库，其中包含用于模拟和混淆攻击的各种 2D 和 3D 演示攻击。来自颜色、深度、近红外和热等不同通道的数据可用于推进面部 PAD 的研究。将所提出的方法与基于特征的方法进行比较，发现其性能优于基线，在引入的数据集上实现了 0.3% 的 ACER。数据库和重现结果的软件是公开提供的。