Robust face anti-spoofing with depth information

With the prevalence of face authentication applications, the prevention of malicious attack from fake faces such as photos or videos, i.e., face anti-spoofing, has attracted much attention recently. However, while an increasing number of works on the face anti-spoofing have been reported based on 2D RGB cameras, most of them cannot handle various attacking methods. In this paper we propose a robust representation jointly modeling 2D textual information and depth information for face anti-spoofing. The textual feature is learned from 2D facial image regions using a convolutional neural network (CNN), and the depth representation is extracted from images captured by a Kinect. A face in front of the camera is classified as live if it is categorized as live using both cues. We collected a face anti-spoofing experimental dataset with depth information, and reported extensive experimental results to validate the robustness of the proposed method.