Abstract. 3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention. Recently, remote Photoplethysmography (rPPG) is employed as an intrinsic liveness cue which is independent of the mask appearance. Although existing rPPGbased methods achieve promising results on both intra and cross dataset scenarios, they may not be robust enough when rPPG signals are contaminated by noise. In this paper, we propose a new liveness feature, called rPPG correspondence feature (CFrPPG) to precisely identify the heartbeat vestige from the observed noisy rPPG signals. To further overcome the global interferences, we propose a novel learning strategy which incorporates the global noise within the CFrPPG feature. Extensive experiments indicate that the proposed feature not only outperforms the state-of-the-art rPPG based methods on 3D mask attacks but also be able to handle the practical scenarios with dim light and camera motion.