Multiresolution synthetic fingerprint generation

Public access to existing high-resolution databases was discontinued. Besides, a hybrid database that contains fingerprints of different sensors with high and medium resolutions does not exist. A novel hybrid approach to synthesise realistic, multiresolution, and multisensor fingerprints to address these issues is presented. The first step was to improve Anguli, a handcrafted fingerprint generator, to create pores, scratches, and dynamic ridge maps. Using CycleGAN, then the maps are converted into realistic fingerprints, adding textures to images. Unlike other neural network-based methods, the authors’ method generates multiple images with different resolutions and styles for the same identity. With the authors’ approach, a synthetic database with 14,800 fingerprints is built. Besides that, fingerprint recognition experiments with pore- and minutiae-based matching techniques and different fingerprint quality analyses are conducted to confirm the similarity between real and synthetic databases. Finally, a human classification analysis is performed, where volunteers could not distinguish between authentic and synthetic fingerprints. These experiments demonstrate that the authors’ approach is suitable for supporting further fingerprint recognition studies in the absence of real databases.