A 3D Mask Face Anti-Spoofing Database With Real World Variations

Siqi Liu, Baoyao Yang, Pong C. Yuen, Guoying Zhao; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 100-106

3D mask face spoofing attack becomes a new challenge and attracts more research interests recently. However, due to the deficiency number and limited variations of database, there are few methods be proposed to aim on it. Meanwhile, most of existing databases only concentrate on the anti-spoofing of different kinds of attacks and ignore the environmental changes in real world applications. In this paper, we build a new 3D mask anti-spoofing database with more variations to simulate the real world scenario. The proposed database contains 12 masks from two companies with different appearance quality. 7 cameras from the stationary and mobile devices and 6 lighting settings that cover typical illumination conditions are also included. Therefore, each subject contains 42 (7 cameras * 6 lightings) genuine and 42 mask sequences and the total size is 1008 videos. Future directions are pointed out in experiments. We plan to release the database to evaluate methods under different variations.