Face presentation attack detection (PAD) is essential for securing the widely used face recognition systems. Most of the existing PAD methods do not generalize well to unseen scenarios because labeled training data of the new domain is usually not available. In light of this, we propose an unsupervised domain adaptation with disentangled representation (DR-UDA) approach to improve the generalization capability of PAD into new scenarios. DR-UDA consists of three modules, i.e., ML-Net, UDA-Net and DR-Net. ML-Net aims to learn a discriminative feature representation using the labeled source domain face images via metric learning. UDA-Net performs unsupervised adversarial domain adaptation in order to optimize the source domain and target domain encoders jointly, and obtain a common feature space shared by both domains. As a result, the source domain PAD model can be effectively transferred to the unlabeled target domain for PAD. DR-Net further disentangles the features irrelevant to specific domains by reconstructing the source and target domain face images from the common feature space. Therefore, DR-UDA can learn a disentangled representation space which is generative for face images in both domains and discriminative for live vs. spoof classification. The proposed approach shows promising generalization capability in several public-domain face PAD databases.

中文翻译：

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用于跨域人脸呈现攻击检测的无监督对抗域适应


人脸呈现攻击检测 (PAD) 对于保护广泛使用的人脸识别系统至关重要。大多数现有的 PAD 方法不能很好地推广到未见过的场景，因为新领域的标记训练数据通常不可用。鉴于此，我们提出了一种具有解缠结表示的无监督域适应（DR-UDA）方法，以提高 PAD 在新场景中的泛化能力。 DR-UDA由三个模块组成，即ML-Net、UDA-Net和DR-Net。 ML-Net 旨在通过度量学习使用标记的源域人脸图像来学习判别性特征表示。 UDA-Net 执行无监督对抗域适应，以联合优化源域和目标域编码器，并获得两个域共享的公共特征空间。结果，源域PAD模型可以有效地转移到未标记的PAD目标域。 DR-Net 通过从公共特征空间重建源域和目标域人脸图像，进一步解开与特定域无关的特征。因此，DR-UDA 可以学习一个解开的表示空间，该空间可生成两个域中的人脸图像，并可区分实时与欺骗分类。所提出的方法在多个公共领域人脸 PAD 数据库中显示出有前景的泛化能力。