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 countermeasures 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.
In REF , the authors inspect the spoofing potential of subject-specific 3D facial masks for 2D face recognition.
Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect
2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)
2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)
Computer Science