论文标题
半监督学习的面部反欺骗的可推广方法
Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning
论文作者
论文摘要
由于生物识别认证系统的高安全性要求，面部抗刺激性引起了很多关注。将面部生物识别带入商业硬件大部分取决于开发可靠的方法，用于检测没有专门传感器的假登录课程。当前基于CNN的方法在训练的域上表现良好，但在以前看不见的数据集上通常表现出较差的概括。在本文中，我们描述了一种利用无监督的预处理来改善多个数据集的性能，而无需进行任何适应，请介绍入门的反poofing数据集以进行监督微调，并提出一个多级辅助分类层，以增强二进制分类的任务，以检测可用于漏洞的示意性的二进制分类任务。我们通过在MSU-MFSD，重播攻击和Oulu-NPU数据集上实现最先进的结果来证明模型的效率。
Face anti-spoofing has drawn a lot of attention due to the high security requirements in biometric authentication systems. Bringing face biometric to commercial hardware became mostly dependent on developing reliable methods for detecting fake login sessions without specialized sensors. Current CNN-based method perform well on the domains they were trained for, but often show poor generalization on previously unseen datasets. In this paper we describe a method for utilizing unsupervised pretraining for improving performance across multiple datasets without any adaptation, introduce the Entry Antispoofing Dataset for supervised fine-tuning, and propose a multi-class auxiliary classification layer for augmenting the binary classification task of detecting spoofing attempts with explicit interpretable signals. We demonstrate the efficiency of our model by achieving state-of-the-art results on cross-dataset testing on MSU-MFSD, Replay-Attack, and OULU-NPU datasets.
