CloudNet: A LiDAR-Based Face Anti-Spoofing Model That Is Robust Against Light Variation

Abstract

Face anti-spoofing (FAS) is a technology that protects face recognition systems from presentation attacks. The current challenge faced by FAS studies is the difficulty in creating a generalized light variation model. This is because face data are sensitive to light domain. FAS models using only red green blue (RGB) images suffer from poor performance when the training and test datasets have different light variations. To overcome this problem, this study focuses on light detection and ranging (LiDAR) sensors. LiDAR is a time-of-flight depth sensor that is included in the latest mobile devices. It is negligibly affected by light and provides 3D coordinate and depth information of the target. Thus, a model that is resistant to light variations and exhibiting excellent performance can be created. For the experiment, datasets collected with a LiDAR camera are built and CloudNet architectures for RGB, point clouds, and depth are designed. Three protocols are used to confirm the performance of the model according to variations in the light domain. Experimental results indicate that for protocols 2 and 3, CloudNet error rates increase by 0.1340 and 0.1528, whereas the error rates of the RGB model increase by 0.3951 and 0.4111, respectively, as compared with protocol 1. These results demonstrate that the LiDAR-based FAS model with CloudNet has a more generalized performance compared with the RGB model.

Keywords

• Deep learning
• face anti-spoofing
• LiDAR
• point cloud