  3D mask face presentation attack detection (PAD) plays a vital role in
securing face recognition systems from emergent 3D mask attacks. Recently,
remote photoplethysmography (rPPG) has been developed as an intrinsic liveness
clue for 3D mask PAD without relying on the mask appearance. However, the rPPG
features for 3D mask PAD are still needed expert knowledge to design manually,
which limits its further progress in the deep learning and big data era. In
this letter, we propose a pure rPPG transformer (TransRPPG) framework for
learning intrinsic liveness representation efficiently. At first, rPPG-based
multi-scale spatial-temporal maps (MSTmap) are constructed from facial skin and
background regions. Then the transformer fully mines the global relationship
within MSTmaps for liveness representation, and gives a binary prediction for
3D mask detection. Comprehensive experiments are conducted on two benchmark
datasets to demonstrate the efficacy of the TransRPPG on both intra- and
cross-dataset testings. Our TransRPPG is lightweight and efficient (with only
547K parameters and 763M FLOPs), which is promising for mobile-level
applications.
