  Spoofing attacks are a threat to modern face recognition systems. In this
work we present a simple yet effective liveness detection approach to enhance
2D face recognition methods and make them robust against spoofing attacks. We
show that the risk to spoofing attacks can be re- duced through the use of an
additional source of light, for example a flash. From a pair of input images
taken under different illumination, we define discriminative features that
implicitly contain facial three-dimensional in- formation. Furthermore, we show
that when multiple sources of light are considered, we are able to validate
which one has been activated. This makes possible the design of a highly secure
active-light authentication framework. Finally, further investigating the use
of 3D features without 3D reconstruction, we introduce an approximated
disparity-based implicit 3D feature obtained from an uncalibrated stereo-pair
of cameras. Valida- tion experiments show that the proposed methods produce
state-of-the-art results in challenging scenarios with nearly no feature
extraction latency.
