Spoofing Attacks To 2D Face Recognition Systems With 3D Masks

Vulnerability to spoofing attacks is a serious drawback for many biometric systems. Among all biometric traits, face is the one that is exposed to the most serious threat, since it is exceptionally easy to access. The limited work on fraud detection capabilities for face mainly shapes around 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices. A significant portion of this work is based on the flatness of the facial surface in front of the sensor. In this study, we complicate the spoofing problem further by introducing the 3rd dimension and ex- amine possible 3D attack instruments. A small database is constructed with six different types of 3D facial masks and it is utilized to conduct experiments on state-of-the-art 2D face recognition systems. Spoofing performance for each type of mask is assessed and analysed thoroughly

Spoofing Face Recognition with 3D Masks

Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In the literature, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type. In order to assess the spoofing performance of 3D masks against 2D, 2.5D and 3D face recognition and to analyse various texture based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two datasets: The Morpho database which is not publicly available and the newly distributed 3D mask attack database