School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2023 Apr 18;23(8):4077. doi: 10.3390/s23084077.
Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing.
人脸反欺骗对于提高人脸识别系统抵御呈现攻击的鲁棒性至关重要。现有的方法主要依赖于二分类任务。最近，基于领域泛化的方法取得了有希望的结果。然而，由于各个领域之间的分布差异，与域相关的特征空间中的差异极大地阻碍了来自不熟悉域的特征的泛化。在这项工作中，我们提出了一种多域特征对齐框架（MADG），用于解决在分散的特征空间中分布的多个源域时出现的概括能力差的问题。具体来说，设计了一个对抗性学习过程来缩小域之间的差异，实现了多源特征对齐的效果，从而实现了多域对齐。此外，为了进一步提高我们提出的框架的有效性，我们结合了多方向三元组损失，以在假脸和真脸之间的特征空间中实现更高的分离度。为了评估我们方法的性能，我们在几个公共数据集上进行了广泛的实验。结果表明，我们提出的方法优于当前最先进的方法，从而验证了其在人脸反欺骗中的有效性。
