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.

翻译：检测人脸识别系统中的3D面具攻击具有挑战性。尽管真实人脸与3D面具在远程光电容积描记（rPPG）信号上存在显著差异，但基于rPPG的人脸反欺骗方法常因视频序列中的面部对齐不稳定及rPPG信号微弱而导致性能下降。为以运动鲁棒方式增强rPPG信号，提出一种基于地标锚定的面部拼接方法，该方法联合使用SIFT关键点和面部地标，在像素级实现稳健精准的面部对齐。为更好编码rPPG信号，提出一种加权时空表示，该表示强化了富含血管的面部区域。此外，联合利用不同颜色空间中rPPG信号的特征。为提升泛化能力，设计了一种集成门控循环单元（GRU）的轻量级EfficientNet，用于从rPPG时空表示中提取空间与时间特征进行分类。在五个基准数据集上，通过数据集内评估与跨数据集评估，将该方法与现有最优方法进行了比较。实验表明，该方法在基于rPPG的人脸欺骗检测中，相较于其他最优方法展现出显著且一致的性能提升。