  A new software-based liveness detection approach using a novel fingerprint
parameterization based on quality related features is proposed. The system is
tested on a highly challenging database comprising over 10,500 real and fake
images acquired with five sensors of different technologies and covering a wide
range of direct attack scenarios in terms of materials and procedures followed
to generate the gummy fingers. The proposed solution proves to be robust to the
multi-scenario dataset, and presents an overall rate of 90% correctly
classified samples. Furthermore, the liveness detection method presented has
the added advantage over previously studied techniques of needing just one
image from a finger to decide whether it is real or fake. This last
characteristic provides the method with very valuable features as it makes it
less intrusive, more user friendly, faster and reduces its implementation
costs.
