Institute of Optoelectronics, Military University of Technology, 2 Gen. S. Kaliskiego St., 00-908 Warsaw, Poland.
Sensors (Basel). 2020 Jul 17;20(14):3988. doi: 10.3390/s20143988.
Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject's state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.
人脸识别系统面临着来自各种伪造攻击的真正挑战。新的、更复杂的伪造攻击方法正变得越来越难以被传统的人脸识别系统检测到。热红外成像提供了一些特定的物理特性，可能会提高伪造攻击检测能力。本文旨在介绍在各种条件下（包括面具的热加热和受试者的各种状态）在热红外中检测各种人脸呈现攻击的调查结果。使用打印和显示的面部照片、3D 打印、定制的柔性 3D 乳胶和硅胶面具对呈现攻击进行了彻底的分析。本文介绍了在长时间实验过程中特定面部特征点的热能分布强度分析。研究了热平衡的影响，以及由于在呈现攻击检测方面的身体努力而导致的主体状态的变化。引入了一个新的热人脸欺骗数据集。最后，提出了一种基于两步深度学习的呈现攻击检测方法。展示了在各种呈现攻击工具上的一组深度学习方法的验证结果。
