Face spoofing detection under super-realistic 3D wax face attacks

Face spoofing attacks based on 3D face images have posed a severe security risk to face recognition systems. Despite the great effort made by the technical community in recent years, existing 3D face spoofing databases, mostly based on 3D masks, still suffer from small sample size, low diversity, or poor authenticity due to the production difficulty and high cost. To fill in this gap, we introduce a new database in this paper with 4-000 single wax figure faces, named SWFFD (Single Wax Figure Face Database), as a type of super-realistic 3D face presentation attack. Collected from online resources, this database has high diversity in terms of subjects, lighting conditions, facial poses, and recording devices. We have also designed a new detection method, which combines attention-aware features from different face scales to generate discriminative representations for realistic face spoofing attack detection. Extensive experiments have been conducted on the SWFFD as well as the CelebA-HQ database (containing real faces from the online collection). Experimental results have demonstrated the effectiveness of the proposed method in both intra-database and cross-database testing scenarios.