Biometric Antispoofing Methods: A Survey in Face Recognition <s> 6) 3D MASK-ATTACK DB <s> The problem of detecting face spoofing attacks (presentation attacks) has recently gained a well-deserved popularity. 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. In this paper, we inspect the spoofing potential of subject-specific 3D facial masks for 2D face recognition. Additionally, we analyze Local Binary Patterns based coun-termeasures using both color and depth data, obtained by Kinect. For this purpose, we introduce the 3D Mask Attack Database (3DMAD), the first publicly available 3D spoofing database, recorded with a low-cost depth camera. Extensive experiments on 3DMAD show that easily attainable facial masks can pose a serious threat to 2D face recognition systems and LBP is a powerful weapon to eliminate it. <s> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 6) 3D MASK-ATTACK DB <s> 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 this paper, 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 analyze various texture-based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two data sets: the Morpho database which is not publicly available and the newly distributed 3D mask attack database. <s> BIB002
The 3D MASK-ATTACK DB BIB001 , BIB002 is publicly available at the IDIAP Research Institute website.  It constitutes the first public database that considers mask attacks and that, in addition to 2D data, provides as well depth information. It comprises genuine and attack access attempts of 17 different users. The attacks were performed by one single operator wearing the real-size 3D masks of the genuine subjects. Masks were manufactured using the service provided by ThatsMyFace.com,  which only requires a frontal and two profile pictures of each person to generate a 3D mask. These three pictures (frontal and profiles) are also distributed with the database. The database was captured in three different sessions: two real-access sessions held two weeks apart and a third session in which the mask attacks were performed. In each session and for each user, five videos of 10 seconds were captured using the Microsoft Kinect for Xbox 360. This sensor provides both regular 2D RGB data (8-bit) and depth data (11-bit) , with a resolution of 640 × 480 pixels at 30 frames per second. Therefore, the data available are: 255 color videos of 300 frames (170 real sequences and 85 mask attacks), and as many 2.5D sequences with the corresponding depth information. Such diversity of data permits a great flexibility for research in the field of face security to direct attacks, since it gives the possibility to study both 2D and 3D anti-spoofing algorithms and their fusion.