论文标题
多帧的时间异常线索学习方法
Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing
论文作者
论文摘要
面部反欺骗研究被广泛用于面部识别，并受到了行业和学者的更多关注。在本文中，我们提出了Eulernet，这是一个新的时间特征融合网络，其中差分过滤器和残留金字塔分别用于从连续帧中提取和扩增异常线索。基于面部标志的轻量级样品标签方法旨在以较低的成本标记大型样品，并且比其他方法（例如3D摄像头）具有更好的结果。最后，我们使用各种移动端来收集30,000个实时和欺骗样本，以创建一个数据集，该数据集在现实世界中复制各种形式的攻击。公共Oulu-NPU进行的广泛实验表明，我们的算法优于最先进的现状，我们的解决方案已经部署在为数百万用户提供服务的现实世界系统中。
Face anti-spoofing researches are widely used in face recognition and has received more attention from industry and academics. In this paper, we propose the EulerNet, a new temporal feature fusion network in which the differential filter and residual pyramid are used to extract and amplify abnormal clues from continuous frames, respectively. A lightweight sample labeling method based on face landmarks is designed to label large-scale samples at a lower cost and has better results than other methods such as 3D camera. Finally, we collect 30,000 live and spoofing samples using various mobile ends to create a dataset that replicates various forms of attacks in a real-world setting. Extensive experiments on public OULU-NPU show that our algorithm is superior to the state of art and our solution has already been deployed in real-world systems servicing millions of users.
