  Detecting 3D mask attacks to a face recognition system is challenging.
Although genuine faces and 3D face masks show significantly different remote
photoplethysmography (rPPG) signals, rPPG-based face anti-spoofing methods
often suffer from performance degradation due to unstable face alignment in the
video sequence and weak rPPG signals. To enhance the rPPG signal in a
motion-robust way, a landmark-anchored face stitching method is proposed to
align the faces robustly and precisely at the pixel-wise level by using both
SIFT keypoints and facial landmarks. To better encode the rPPG signal, a
weighted spatial-temporal representation is proposed, which emphasizes the face
regions with rich blood vessels. In addition, characteristics of rPPG signals
in different color spaces are jointly utilized. To improve the generalization
capability, a lightweight EfficientNet with a Gated Recurrent Unit (GRU) is
designed to extract both spatial and temporal features from the rPPG
spatial-temporal representation for classification. The proposed method is
compared with the state-of-the-art methods on five benchmark datasets under
both intra-dataset and cross-dataset evaluations. The proposed method shows a
significant and consistent improvement in performance over other
state-of-the-art rPPG-based methods for face spoofing detection.
