Fingerprint Liveness Detection Based on Quality Measures
This addresses security vulnerabilities in fingerprint authentication systems by improving detection of fake fingerprints, though it appears incremental as it builds on existing quality measure approaches.
The paper tackles fingerprint liveness detection by proposing a new parameterization based on quality measures, achieving a 93% correct classification rate on a dataset of over 4,500 images from three sensors.
A new fingerprint parameterization for liveness detection based on quality measures is presented. The novel feature set is used in a complete liveness detection system and tested on the development set of the LivDET competition, comprising over 4,500 real and fake images acquired with three different optical sensors. The proposed solution proves to be robust to the multi-sensor scenario, and presents an overall rate of 93% of 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.