A light field camera is a sensor that can record the directions as well as the colors of incident rays. This camera is widely utilized from 3D reconstruction to face and iris recognition. In this paper, we suggest a novel approach for defending spoofing face attacks, like printed 2D facial photos (hereinafter 2D photos) and HD tablet images, using the light field camera. By viewing the raw light field photograph from a different standpoint, we extract two special features which cannot be obtained from the conventional camera. To verify the performance, we compose light field photograph databases and conduct experiments. Our proposed method achieves at least 94.78% accuracy or up to 99.36% accuracy under different types of spoofing attacks.

In order to develop security systems for identity authentication, face recognition (FR) technology has been applied. One of the main problems of applying FR technology is that the systems are especially vulnerable to attacks with spoofing faces (e.g., 2D pictures). To defend from these attacks and to enhance the reliability of FR systems, many anti-spoofing approaches have been recently developed. In this paper, we propose a method for face liveness detection using the effect of defocus. From two images sequentially taken at different focuses, three features, focus, power histogram and gradient location and orientation histogram (GLOH), are extracted. Afterwards, we detect forged faces through the feature-level fusion approach. For reliable performance verification, we develop two databases with a handheld digital camera and a webcam. The proposed method achieves a 3.29% half total error rate (HTER) at a given depth of field (DoF) and can be extended to camera-equipped devices, like smartphones.