Abstract
Face anti-spoofing is crucial to the security of face recognition systems. Previously, most methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks (PA). However, new attack methods keep evolving that produce new forms of spoofing faces to compromise the existing detectors. This requires researchers to collect a large number of samples to train classifiers for detecting new attacks, which is often costly and leads the later newly evolved attack samples to remain in small scales. Alternatively, we define face anti-spoofing as a few-shot learning problem with evolving new attacks and propose a novel face anti-spoofing approach via meta-learning named Meta Face Anti-spoofing (Meta-FAS). Meta-FAS addresses the above-mentioned problems by training the classifiers how to learn to detect the spoofing faces with few examples. To assess the effectiveness of the proposed approach, we propose a series of evaluation benchmarks based on public datasets (\textit{e.g.}, OULU-NPU, SiW, CASIA-MFSD, Replay-Attack, MSU-MFSD, 3D-MAD, and CASIA-SURF), and the proposed approach shows its superior performances to compared methods.
Abstract (translated)
人脸反欺骗对人脸识别系统的安全至关重要。以前，大多数方法都将人脸反欺骗作为一个有监督的学习问题来检测各种预定义的表示攻击（PA）。然而，新的攻击方法不断发展，产生新形式的欺骗面，以危害现有的探测器。这就要求研究人员收集大量的样本来训练分类器来检测新的攻击，这通常是昂贵的，并导致后来新进化的攻击样本保持小规模。或者，我们将人脸反欺骗定义为一个不断发展的新攻击的少数镜头学习问题，并通过名为meta face反欺骗（meta fas）的元学习提出了一种新的人脸反欺骗方法。meta fas通过训练分类器如何学习用很少的例子检测欺骗面来解决上述问题。为了评估所提出方法的有效性，我们提出了一系列基于公共数据集的评估基准（如、oulu-npu、siw、casia-mfsd、replay attack、msu-mfsd、3d-mad和casia-surf），所提出的方法显示了其优于比较方法的性能。
URL
https://arxiv.org/abs/1904.12490
