School of Computing, Robert Gordon University, Aberdeen AB10 7AQ, UK.
Sensors (Basel). 2022 Jul 12;22(14):5196. doi: 10.3390/s22145196.
Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal performance on PA detection tasks. In this paper, we present a simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features. Extensive experiments clearly show the benefit of enriching the feature space to improve detection rates by using three common public datasets, namely CASIA, Replay Attack, and SiW. This work opens future research to improve face presentation attack detection by exploring new characterizing features and fusion strategies.
人脸呈现攻击（PA）是人脸识别（FR）应用的严重威胁。这些攻击易于执行且难以检测。只需将视频、照片或面具呈现给相机，即可进行攻击。文献表明，基于现代、预训练、深度学习的方法和传统的手工制作、特征工程方法都有效地检测到了 PA。然而，仍然存在一个问题，即现有的深度神经网络中学习到的特征是否足以包含传统的低层次特征，以便在 PA 检测任务上实现最佳性能。在本文中，我们提出了一种简单的特征融合方法，该方法将使用预训练的深度学习模型提取的特征与更传统的颜色和纹理特征相结合。广泛的实验清楚地表明，通过使用三个常见的公共数据集（即 CASIA、Replay Attack 和 SiW）丰富特征空间以提高检测率是有益的。这项工作为通过探索新的特征描述符和融合策略来改进人脸呈现攻击检测开辟了未来的研究方向。
