Face anti-spoofing is crucial to security of face recognition systems.
Previous approaches focus on developing discriminative models based on the
features extracted from images, which may be still entangled between spoof
patterns and real persons. In this paper, motivated by the disentangled
representation learning, we propose a novel perspective of face anti-spoofing
that disentangles the liveness features and content features from images, and
the liveness features is further used for classification. We also put forward a
Convolutional Neural Network (CNN) architecture with the process of
disentanglement and combination of low-level and high-level supervision to
improve the generalization capabilities. We evaluate our method on public
benchmark datasets and extensive experimental results demonstrate the
effectiveness of our method against the state-of-the-art competitors. Finally,
we further visualize some results to help understand the effect and advantage
of disentanglement.

本文提出了一种新型的人脸反欺诈方法，旨在通过解缠活体特征和内容特征从图像中提取最终进行分类的活体特征，并采用卷积神经网络和低层级和高层级监控的结合来提高其泛化能力，并在公共基准数据集上进行了充分的实验结果验证。