Face spoofing detection using a light field imaging framework
Face recognition systems are becoming ubiquitous, but they are vulnerable to spoofing attacks. The recently available light field cameras can be used for spoofing attack detection. In this study, the IST Lenslet Light Field Face Spoofing Database (IST LLFFSD) is proposed, consisting of 100 genuine images, from 50 subjects, captured with a Lytro ILLUM lenslet light field camera, and a set of 600 face spoofing attack images, captured using the same camera. The IST LLFFSD simulates six different types of presentation attacks, including printed paper, wrapped printed paper, laptop, tablet and two different mobile phones. This study also proposes a novel spoofing attack detection solution, based on a compact, yet effective, descriptor exploiting the colour and texture variations associated with the different directions of light captured in light field images. Extensive experiments show very effective results, with the proposed solution performing better than state-of-the-art alternatives for the face spoofing attack types considered.