Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks

The restrictions posed by the recent trans-border regulations to the usage of biometric data force researchers in the fields of digitized forensics and biometrics to use synthetic data for development and evaluation of new algorithms. For digitized forensics, we introduce a technique for conversion of privacy-sensitive datasets of real latent fingerprints to "privacy-friendly" datasets of synthesized fingerprints. Privacy-friendly means in our context that the generated fingerprint images cannot be linked to a particular person who provided fingerprints to the original dataset. In contrast to the standard fingerprint generation approach that makes use of mathematical modeling for drawing ridge-line patterns, we propose applying a data-driven approach making use of generative adversarial neural networks (GAN). In our synthesis experiments the performance of three established GAN architectures is examined. The NIST Special Database 27 is exemplary used as a data source of real latent fingerprints. The set of training images is augmented by applying filters from the StirTrace benchmarking tool. The suitability of the generated fingerprint images is checked with the NIST fingerprint image quality tool (NFIQ2). The unlinkability to any original fingerprint is established by evaluating outcomes of the NIST fingerprint matching tool.