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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相机不变特征学习用于广义人脸反欺骗

在基于学习的面部反欺骗中，越来越多的共识是，相机模型的差异会在实际应用场景中造成较大的领域差距。我们描述了一个框架，该框架在功能级别消除了来自采集相机的固有方差的影响，从而导致了可以高度适应不同采集设备的广义面部欺骗检测模型。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别分解来学习相机不变的欺骗特征。受到欺骗特征的影响不仅存在于高频域，在第二分支中，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，该方法在数据集内和跨数据集设置中均可实现较好的性能，证明了在各种应用场景下的高泛化能力。