Multi-Channel Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection

With the advancement of 3D printing technologies, 3D mask presentation attack becomes a critical challenge in face recognition. To tackle the 3D mask presentation attack detection (PAD), remote Photoplethysmography (rPPG) is employed as an intrinsic detection cue which is independent of the mask material and appearance quality. Although the effectiveness of existing rPPG-based methods has been verified, they may not be robust enough when rPPG signals are contaminated by noise. To identify the heartbeat information from the noisy raw rPPG signals, we propose a new 3D mask PAD feature, multi-channel rPPG correspondence feature (MCCFrPPG) with the global noise-aware template learning and verification framework. To further boost the discriminability, temporal variation of the rPPG signal is considered and extracted through the multi-channel time-frequency analysis scheme. This paper also extends HKBU-MARs V2 dataset with more customized high-quality masks and increases the number of videos by two times. Comprehensive experiments were performed on existing 3D mask datasets and the extended HKBU-MARs V2+, which totally covers 3 types of masks, 12 different light settings and 6 cameras. The results not only justify the effectiveness and robustness of the proposed MCCFrPPG on 3D mask attacks but also indicate its potential on handling the replay attack with camera motion and dim light.