RFace: Anti-Spoofing Facial Authentication Using COTS RFID

Current facial authentication (FA) systems are mostly based on the images of human faces, thus suffering from privacy leakage and spoofing attacks. Mainstream systems utilize facial geometry features for spoofing mitigation, which are still easy to deceive with the feature manipulation, e.g., 3D-printed human faces. In this paper, we propose a novel privacy-preserving anti-spoofing FA system, named RFace, which extracts both the 3D geometry and inner biomaterial features of faces using a COTS RFID tag array. These features are difficult to obtain and forge, hence are resistant to spoofing attacks. RFace only requires users to pose their faces in front of a tag array for a few seconds, without leaking their visual facial information. We build a theoretical model to rigorously prove the feasibility of feature acquisition and the correlation between the facial features and RF signals. For practicality, we design an effective algorithm to mitigate the impact of unstable distance and angle deflection from the face to the array. Extensive experiments with 30 participants and three types of spoofing attacks show that RFace achieves an average authentication success rate of over 95.7% and an EER of 4.4%. More importantly, no spoofing attack succeeds in deceiving RFace in the experiments.