Face anti-spoofing aims to detect presentation attack to face recognition--based authentication systems. It has drawn growing attention due to the high security demand. The widely adopted CNN-based methods usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of novel patterns or unseen scenes, leading to poor generalization performance. Furthermore, almost all current methods treat face anti-spoofing as a prior step to face recognition, which prolongs the response time and makes face authentication inefficient. In this article, we try to boost the generalizability and applicability of face anti-spoofing methods by designing a new generalizable face authentication CNN (GFA-CNN) model with three novelties. First, GFA-CNN introduces a simple yet effective total pairwise confusion loss for CNN training that properly balances contributions of all spoofing patterns for recognizing the spoofing faces. Second, it incorporate a fast domain adaptation component to alleviate negative effects brought by domain variation. Third, it deploys filter diversification learning to make the learned representations more adaptable to new scenes. In addition, the proposed GFA-CNN works in a multi-task manner—it performs face anti-spoofing and face recognition simultaneously. Experimental results on five popular face anti-spoofing and face recognition benchmarks show that GFA-CNN outperforms previous face anti-spoofing methods on cross-test protocols significantly and also well preserves the identity information of input face images.

中文翻译：

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学习用于人脸反欺骗的可泛化和身份识别表示

人脸反欺骗旨在检测对基于人脸识别的身份验证系统的演示攻击。由于高安全性需求，它引起了越来越多的关注。广泛采用的基于 CNN 的方法通常在训练和测试显示相似模式的欺骗样本时能很好地识别欺骗人脸，但在测试新模式或看不见的场景的欺骗人脸时，它们的性能会急剧下降，导致泛化性能不佳。此外，目前几乎所有的方法都将人脸反欺骗作为人脸识别的先行步骤，这延长了响应时间，使人脸认证效率低下。在本文中，我们尝试通过设计具有三个新颖性的新的可泛化人脸认证 CNN (GFA-CNN) 模型来提高人脸反欺骗方法的通用性和适用性。第一的，GFA-CNN 为 CNN 训练引入了一种简单而有效的总成对混淆损失，可以适当地平衡所有欺骗模式对识别欺骗人脸的贡献。其次，它结合了快速域适应组件，以减轻域变化带来的负面影响。第三，它部署了过滤器多样化学习，使学习的表示更适应新场景。此外，所提出的 GFA-CNN 以多任务方式工作——它同时执行人脸反欺骗和人脸识别。在五个流行的人脸反欺骗和人脸识别基准上的实验结果表明，GFA-CNN 在交叉测试协议上明显优于以前的人脸反欺骗方法，并且很好地保留了输入人脸图像的身份信息。