论文标题
创建人工模式来解决RGB的恶化
Creating Artificial Modalities to Solve RGB Liveness
论文作者
论文摘要
为面部抗旋转提供有用功能的特殊摄像机是可取的，但并非总是一种选择。在这项工作中，我们提出了一种通过创建RGB视频的人工方式来利用善意和欺骗样本之间动态外观差异的方法。我们引入了两种类型的人工变换：秩和光流，结合端到端管道进行欺骗检测。我们证明，使用包含较少身份和细粒度特征的中间表示形式增加了模型的鲁棒性，从而使人看不见攻击以及看不见的种族。所提出的方法在最大的跨种族上实现了最先进的方法。
Special cameras that provide useful features for face anti-spoofing are desirable, but not always an option. In this work we propose a method to utilize the difference in dynamic appearance between bona fide and spoof samples by creating artificial modalities from RGB videos. We introduce two types of artificial transforms: rank pooling and optical flow, combined in end-to-end pipeline for spoof detection. We demonstrate that using intermediate representations that contain less identity and fine-grained features increase model robustness to unseen attacks as well as to unseen ethnicities. The proposed method achieves state-of-the-art on the largest cross-ethnicity face anti-spoofing dataset CASIA-SURF CeFA (RGB).
