Level Three Synthetic Fingerprint Generation
This addresses the bottleneck in fingerprint recognition research for biometrics and security applications by providing a legal alternative to real data, though it is an incremental improvement over existing synthesis methods.
The authors tackled the problem of limited public access to high-resolution fingerprint databases due to privacy restrictions by developing a hybrid method to synthesize realistic fingerprints, resulting in a database of 7400 images where human volunteers could hardly distinguish real from synthetic fingerprints and performance matched real databases.
Today's legal restrictions that protect the privacy of biometric data are hampering fingerprint recognition researches. For instance, all high-resolution fingerprint databases ceased to be publicly available. To address this problem, we present a novel hybrid approach to synthesize realistic, high-resolution fingerprints. First, we improved Anguli, a handcrafted fingerprint generator, to obtain dynamic ridge maps with sweat pores and scratches. Then, we trained a CycleGAN to transform these maps into realistic fingerprints. Unlike other CNN-based works, we can generate several images for the same identity. We used our approach to create a synthetic database with 7400 images in an attempt to propel further studies in this field without raising legal issues. We included sweat pore annotations in 740 images to encourage research developments in pore detection. In our experiments, we employed two fingerprint matching approaches to confirm that real and synthetic databases have similar performance. We conducted a human perception analysis where sixty volunteers could hardly differ between real and synthesized fingerprints. Given that we also favorably compare our results with the most advanced works in the literature, our experimentation suggests that our approach is the new state-of-the-art.