A high performance fingerprint liveness detection method based on quality related features
This addresses security vulnerabilities in fingerprint authentication systems against spoofing attacks, though it appears incremental as it builds on existing liveness detection techniques.
The paper tackles fingerprint liveness detection by proposing a software-based method using quality-related features, achieving 90% correct classification on a challenging dataset of over 10,500 real and fake images from five sensors.
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.