There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.

中文翻译：

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用于广义人脸反欺骗的相机不变特征学习


基于学习的人脸反欺骗越来越多地达成共识，即相机模型的差异导致实际应用场景中存在巨大的领域差距。我们描述了一个框架，该框架在特征级别上消除了采集相机固有差异的影响，从而产生了可以高度适应不同采集设备的广义人脸欺骗检测模型。特别是，该框架由两个分支组成。第一个分支旨在通过高频域中的特征级分解来学习相机不变的欺骗特征。由于欺骗特征不仅存在于高频域，在第二个分支中，基于高频和低频信息的重组，从增强图像中进一步增强了提取的欺骗特征的辨别能力。最后，通过加权策略将两个分支的分类结果融合在一起。实验表明，所提出的方法在数据集内和跨数据集设置中都能取得更好的性能，在各种应用场景中展现出较高的泛化能力。