Demonstrating the Vulnerability of RGB-D based Face Recognition to GAN-generated Depth-map Injection

RGB-D cameras are devices able to collect additional information, compared to classical RGB devices, about the observed scene: its depth (D). This has made RGB-D very suitable for many image processing tasks, including presentation attack detection (PAD) in face recognition systems. This work aims at demonstrating that thanks to novel techniques developed in recent years, such as generative adversarial networks (GANs), face PAD systems based on RGB-D are now vulnerable to logical access attack. In this work, a GAN is trained to generate a depth map from an input 2D RGB face image. The attacker can then fool the system by injecting a photo of the authorized user along with the generated depth map. Among all RGB-D devices, this work focuses on light-field cameras but the proposed framework can be easily adapted for other RGB-D devices. The GAN is trained on the IST-EURECOM light-field face database (LFFD). The attack is simulated thanks to the IST lenslet light field face spoofing database (LLFFSD). A third dataset is used to show that the proposed approach generalizes well on a different face database.