In this article, we propose a face spoofing detection method by learning to fuse high-frequency (HF) and low-frequency (LF) features, in an effort to improve the generalization capability and fill up the domain gap between training and testing when the antispoofing is practically conducted in unseen scenarios. In particular, the proposed face antispoofing model consists of two streams that extract HF and LF components of a facial image with three high-pass and three low-pass filters. Moreover, considering the fact that spoofing features exist in different feature levels, we train our network with a novel multiscale triplet loss. The cross-frequency spatial attention module further enables the two streams to communicate and exchange information with each other. Finally, the outputs of the two streams are fused with a weighting strategy for final classification. Extensive experiments conducted on intra- and cross-database settings show the superiority of the proposed scheme.

中文翻译：

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通过学习融合高频和低频域中的特征来进行广义人脸反欺骗

在本文中，我们提出了一种通过学习融合高频（HF）和低频（LF）特征的面部欺骗检测方法，以努力提高泛化能力并填补训练和测试之间的空白。反欺骗实际上是在看不见的情况下进行的。特别地，所提出的面部防欺骗模型由两个流组成，这两个流使用三个高通和三个低通滤波器来提取面部图像的HF和LF分量。此外，考虑到欺骗特征存在于不同的特征级别这一事实，我们对网络进行了新颖的多尺度三重态损失训练。跨频空间关注模块还使两个流能够相互通信和交换信息。最后，两个流的输出与加权策略融合在一起，以进行最终分类。在数据库内部和跨数据库设置上进行的大量实验表明了该方案的优越性。