A survey on 3D mask presentation attack detection and countermeasures. (February 2020)

• Record Type: Journal Article
• Authors: Jia, Shan; Guo, Guodong; Xu, Zhengquan
• Is Part Of: Pattern recognition. Volume 98(2020:Feb.)
• Publication Date: 2020-02
• Subjects: Face presentation attack -- 3D Mask spoofing -- Biometrics; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107032
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Despite the impressive progress in face recognition, current systems are vulnerable to presentation attacks, which subvert the face recognition systems by presenting a face artifact. Several techniques have been developed to automatically detect different presentation attacks, mostly for 2D photo print and video replay attacks. However, with the development of 3D modeling and printing technologies, 3D mask has become a more effective way to attack the face recognition systems. Over the last decade, various detection methods for 3D mask attacks have been proposed, but there is no survey yet to summarize the advances. We present a comprehensive overview of the state-of-the-art approaches in 3D mask spoofing and anti-spoofing, including existing databases and countermeasures. In addition, we quantitatively compare the performance of different mask spoofing detection methods on a common ground (i.e., using the same database and evaluation metric). The effectiveness of several 2D presentation attack detection methods is also evaluated on two 3D mask spoofing databases to show whether they are applicable or not for 3D mask attacks. Finally, we present some insights and summarize open issues to address in the future.