Single-Shot Face Anti-Spoofing for Dual Pixel Camera
Xiaojun Wu, Jinghui Zhou, Jun Liu, Fangyi Ni, Haoqiang Fan
Abstract
In this study, we propose a neural network-based face anti-spoofing algorithm using dual pixel (DP) sensor images. The proposed algorithm has two stages: depth reconstruction and depth classification. The first network takes a DP image pair as input and generates a depth map with a baseline of approximately 1 mm. Then, the classification network is trained to distinguish real individuals and planar attack shapes to produce a binary output. A DP image is utilized to estimate the depth map; thus, the proposed face anti-spoofing method is simple and robust. Experimental results demonstrate that the generated depth map helps distinguish real human faces from nonface attack, including images recaptured from photos or screens. The proposed algorithm achieves better anti-spoofing performance compared with other stereo and phase-based depth estimation schemes.