Mask does not matter
Weiye Xu, Wenfan Song, Jianwei Liu, Yajie Liu, Xinzhuang Cui, Yuanqing Zheng, Jinsong Han, Xinhuai Wang, Kui Ren
Abstract
Face authentication (FA) schemes are universally adopted. However, current FA systems are mainly camera-based and hence susceptible to face occlusion (e.g., facial masks) and vulnerable to spoofing attacks (e.g., 3D-printed masks). This paper exploits the penetrability, material sensitivity, and fine-grained sensing capability of millimeter wave (mmWave) to build an anti-spoofing FA system, named mmFace. It scans the human face by moving a commodity off-the-shelf (COTS) mmWave radar along a specific trajectory. The mmWave signals bounced off the human face carry the facial biometric features and structure features, which allows mmFace to achieve reliable liveness detection and FA. Due to the penetrability of mmWave, mmFace can still work well even if users wear masks. We explore a distance-resistant facial structure feature to suppress the impact of unstable face-to-device distance. To avoid inconvenient on-site registration, we also propose a novel virtual registration approach based on the core idea of cross-modal transformation from photos to mmWave signals. We implement mmFace with various antenna configurations and prototype two typical modes of mmFace. Extensive experiments show that mmFace can realize accurate FA as well as reliable liveness detection.