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.

本文提出了一种基于多通道卷积神经网络的一类分类器方法，其中引入了一种新的损失函数，通过一类高斯混合模型实现面部认证攻击检测，该方法的实验结果表明具有较高的泛化能力。