论文标题
抑制域 - 不足的面部抗疾病的欺骗性因素
Suppressing Spoof-irrelevant Factors for Domain-agnostic Face Anti-spoofing
论文作者
论文摘要
面部反欺骗旨在通过区分图像是由人的脸还是欺骗培养基来区分面部识别系统的错误身份验证。我们提出了一种新的方法，称为域 - 不合时式抗旋转的偶然对抗抑制网络（DASN）。 DASN通过学习有效抑制欺骗性的因素（SIF）（例如，摄像机传感器，照明）来提高看不见域的概括能力。为了实现我们的目标，我们介绍了两种类型的对抗学习方案。在第一个对抗性学习方案中，通过部署针对编码器训练的多个歧视头来抑制多个SIF。在第二个对抗学习方案中，每个歧视头部也经过对抗训练以抑制欺骗因素，次级欺骗分类器和编码器的组旨在通过克服抑制来增强欺骗因素。我们在四个公共基准数据集上评估了提出的方法，并获得了显着的评估结果。结果证明了该方法的有效性。
Face anti-spoofing aims to prevent false authentications of face recognition systems by distinguishing whether an image is originated from a human face or a spoof medium. We propose a novel method called Doubly Adversarial Suppression Network (DASN) for domain-agnostic face anti-spoofing; DASN improves the generalization ability to unseen domains by learning to effectively suppress spoof-irrelevant factors (SiFs) (e.g., camera sensors, illuminations). To achieve our goal, we introduce two types of adversarial learning schemes. In the first adversarial learning scheme, multiple SiFs are suppressed by deploying multiple discrimination heads that are trained against an encoder. In the second adversarial learning scheme, each of the discrimination heads is also adversarially trained to suppress a spoof factor, and the group of the secondary spoof classifier and the encoder aims to intensify the spoof factor by overcoming the suppression. We evaluate the proposed method on four public benchmark datasets, and achieve remarkable evaluation results. The results demonstrate the effectiveness of the proposed method.
