Context based Face Anti-Spoofing

The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks and the number of countermeasures is gradually increasing. Fairly good results have been reported on the publicly available databases but it is reasonable to assume that there exists no superior anti-spoofing technique due to the varying nature of attack scenarios and acquisition conditions. Therefore, we propose to approach the problem of face spoofing as a set of attack-specific subproblems that are solvable with a proper combination of complementary countermeasures. Inspired by how we humans can perform reliable spoofing detection only based on the available scene and context information, this work provides the first investigation in research literature that attempts to detect the presence of spoofing medium in the observed scene. We experiment with two publicly available databases consisting of several fake face attacks of different nature under varying conditions and imaging qualities. The experiments show excellent results beyond the state of the art. More importantly, our cross-database evaluation depicts that the proposed approach has promising generalization capabilities.