The functionality of face biometric systems is severely challenged by presentation attacks (PA’s), and especially those attacks that have not been available during the training phase of a PA detection (PAD) subsystem. Among other alternatives, the one-class classification (OCC) paradigm is an applicable strategy that has been observed to provide good generalisation against unseen attacks. Following an OCC approach for the unseen face PAD from RGB images, this work advocates a matrix-regularised multiple kernel learning algorithm to make use of several sources of information each constituting a different view of the face PAD problem. In particular, drawing on the one-class null Fisher classification principle, we characterise different deep CNN representations as kernels and propose a multiple kernel learning (MKL) algorithm subject to an ( r,pr,p )-norm ( 1≤r,p1\leq r,p ) matrix regularisation constraint. The propose MKL algorithm is formulated as a saddle point Lagrangian optimisation task for which we present an effective optimisation algorithm with guaranteed convergence. An evaluation of the proposed one-class MKL algorithm on both general object images in an OCC setting as well as on different face PAD datasets in an unseen zero-shot attack detection setting illustrates the merits of the proposed method compared to other one-class multiple kernel and deep end-to-end CNN-based methods.

中文翻译：

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用于看不见的人脸呈现攻击检测的矩阵正则化一类多核学习


人脸生物识别系统的功能受到演示攻击 (PA) 的严重挑战，尤其是那些在 PA 检测 (PAD) 子系统的训练阶段无法进行的攻击。在其他替代方案中，一类分类 (OCC) 范式是一种适用的策略，已被观察到可以针对看不见的攻击提供良好的泛化能力。遵循从 RGB 图像中处理看不见的人脸 PAD 的 OCC 方法，这项工作提出了一种矩阵正则化的多核学习算法，以利用多个信息源，每个信息源构成人脸 PAD 问题的不同视图。特别是，利用一类零 Fisher 分类原理，我们将不同的深度 CNN 表示表征为内核，并提出了一种遵循 ( r,pr,p )-范数 ( 1≤r,p1 ) 的多内核学习 (MKL) 算法\leq r,p ) 矩阵正则化约束。所提出的 MKL 算法被表述为鞍点拉格朗日优化任务，为此我们提出了一种保证收敛的有效优化算法。对所提出的一类 MKL 算法在 OCC 设置中的一般对象图像以及未见的零样本攻击检测设置中的不同人脸 PAD 数据集上的评估说明了所提出的方法与其他一类多重算法相比的优点基于内核和深度端到端 CNN 的方法。