Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to address this issue, they often fail in generalizing to unseen attacks. In this work, we propose a new framework for PAD using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network ( MCCNN ). A novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks. A one-class Gaussian Mixture Model is used on top of these embeddings for the PAD task. The proposed framework introduces a novel approach to learn a robust PAD system from bonafide and available (known) attack classes. This is particularly important as collecting bonafide data and simpler attacks are much easier than collecting a wide variety of expensive attacks. The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks. Further, we have performed experiments with MLFP and SiW-M datasets using RGB channels only. Superior performance in unseen attack protocols shows the effectiveness of the proposed approach. Software, data, and protocols to reproduce the results are made available publicly.

中文翻译：

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使用多通道卷积神经网络学习用于面部表情攻击检测的一类表示

人脸识别已发展为一种广泛使用的生物特征识别方式。但是，其针对表示攻击的漏洞构成了重大的安全威胁。尽管表示攻击检测（PAD）方法试图解决此问题，但它们通常无法推广到看不见的攻击。在这项工作中，我们提出了一种使用一类分类器的PAD新框架，其中使用的表示是通过多通道卷积神经网络（ 神经网络 ）。引入了一种新颖的损失函数，该函数迫使网络学习用于善意类，而远离攻击的表示形式。在这些嵌入之上，将一类高斯混合模型用于PAD任务。拟议的框架引入了一种新颖的方法来从中学习强大的PAD系统善意和可用（已知）攻击类别。这对于收集非常重要善意数据和更简单的攻击比收集各种昂贵的攻击要容易得多。拟议的系统在公众可获得的条件下进行评估WMCA多渠道人脸PAD数据库，其中包含各种2D和3D攻击。此外，我们进行了MLFP 和 硅钨仅使用RGB通道的数据集。在看不见的攻击协议中具有出色的性能，证明了该方法的有效性。再现结果的软件，数据和协议是公开可用的。