3DMA: A Multi-modality 3D Mask Face Anti-spoofing Database

Benefiting from publicly available databases, face anti-spoofing has recently gained extensive attention in the academic community. However, most of the existing databases focus on the 2D object attacks, including photo and video attacks. The only two public 3D mask face anti-spoofing database are very small. In this paper, we release a multi-modality 3D mask face anti-spoofing database named 3DMA, which contains 920 videos of 67 genuine subjects wearing 48 kinds of 3D masks, captured in visual (VIS) and near-infrared (NIR) modalities. To simulate the real world scenarios, two illumination and four capturing distance settings are deployed during the collection process. To the best of our knowledge, the proposed database is currently the most extensive public database for 3D mask face anti-spoofing. Furthermore, we build three protocols for performance evaluation under different illumination conditions and distances. Experimental results with Convolutional Neural Network (CNN) and LBP-based methods reveal that our proposed 3DMA is indeed a challenge for face anti-spoofing. This database is available at http://www.cbsr.ia.ac.cn/english/3DMA.html. We hope our public 3DMA database can help to pave the way for further research on 3D mask face anti-spoofing.