To tackle the 3D mask face presentation attack, remote Photoplethysmography (rPPG), a biomedical technique that can detect heartbeat signal remotely, is employed as an intrinsic liveness cue. Although existing rPPG-based methods exhibit encouraging results, they require long observation time (10-12 seconds) to identify the heartbeat information, which limits their employment in real applications such as smartphone unlock and e-payment. To shorten the observation time (within 1-second) while keeping the performance, we propose a fast rPPG-based 3D mask presentation attack detection (PAD) method by analyzing the similarity of local facial rPPG signals in the time domain. In particular, a set of temporal similarity features of facial and background local rPPG signals are designed and fused to adapt the real world variations based on rPPG shape and phase properties. For better evaluation under practical variations, we build the HKBU-MARsV2+ dataset that includes 16 masks from 2 types and 6 lighting conditions. Finally, extensive experiments are conducted on 11092 shortterm video slots from 4 datasets with a large number of real- world variations, in terms of mask type, lighting condition, camera, resolution of face region, and compression setting. Results show that the proposed TSrPPG outperforms the state-of-the-art competitors dramatically on discriminabil- ity and generalizability. To our best knowledge, this is the first work that addresses the length of observation time issue of rPPG-based 3D mask PAD.