rPPG-Based Spoofing Detection for Face Mask Attack using Efficientnet on Weighted Spatial-Temporal Representation

Face spoofing detection against paper attack and video-replay attack has been well studied, whereas detecting 3D face mask attack remains challenging. Remote photoplethysmography (rPPG) signal is a recently developed liveness clue for face-spoofing detection. The main challenge of existing rPPG-based methods is that the signal can be easily distorted by background noise or object motion. To address this problem, in this work, we propose an rPPG-based face-spoofing detection method using multiple regions of interests (ROIs) covering entire face, and emphasize the regions containing richer rPPG signals using larger weights. The rPPG signals of these regions form a weighted spatial-temporal map. In view of the discriminant power of EfficientNet over other deep convolutional neural networks, we propose a domain-specific EfficientNet as the classification method. Extensive experiments on two databases namely 3DMAD and HKBU-Mars V2 demonstrate the superior performance of the proposed method over state-of-the-art rPPG-based face-spoofing-detection algorithms.