论文标题
深度频繁的空间暂时性学习面部抗疾病
Deep Frequent Spatial Temporal Learning for Face Anti-Spoofing
论文作者
论文摘要
面部反欺骗对面部识别系统的安全至关重要，避免侵犯演示攻击。先前的工作表明，使用深度和时间监督执行此任务的有效性。但是，经常仅在单个帧中考虑深度监督，并且通过利用某些信号来探索时间监督，而这些信号对场景的更改不稳定。在这项工作中，以两个流探测的驱动，我们提出了一个新颖的两个流freqsaptialtemporamornet，用于面部抗旋转，同时利用了频繁的，空间和时间信息。与现有的方法相比，在多帧RGB图像中欺骗提示的方法，我们将多帧频谱图像作为判别性深神经网络的一个输入流进行，鼓励自动发掘现场和假视频之间的主要区别。广泛的实验显示了使用拟议的体系结构有希望的改进结果。同时，我们提出了一种简洁的方法，通过利用频繁的增强管道来获得大量欺骗培训数据，这在训练大型网络时可以详细可视化和伪造图像之间的详细可视化以及数据不足问题。
Face anti-spoofing is crucial for the security of face recognition system, by avoiding invaded with presentation attack. Previous works have shown the effectiveness of using depth and temporal supervision for this task. However, depth supervision is often considered only in a single frame, and temporal supervision is explored by utilizing certain signals which is not robust to the change of scenes. In this work, motivated by two stream ConvNets, we propose a novel two stream FreqSaptialTemporalNet for face anti-spoofing which simultaneously takes advantage of frequent, spatial and temporal information. Compared with existing methods which mine spoofing cues in multi-frame RGB image, we make multi-frame spectrum image as one input stream for the discriminative deep neural network, encouraging the primary difference between live and fake video to be automatically unearthed. Extensive experiments show promising improvement results using the proposed architecture. Meanwhile, we proposed a concise method to obtain a large amount of spoofing training data by utilizing a frequent augmentation pipeline, which contributes detail visualization between live and fake images as well as data insufficiency issue when training large networks.
