Data-Driven Fingerprint Reconstruction from Minutiae Based on Real and Synthetic Training Data

Abstract

Fingerprint reconstruction from minutiae performed by model-based approaches often lead to fingerprint patterns that lack realism. In contrast, data-driven reconstruction leads to realistic fingerprints, but the reproduction of a fingerprint’s identity remain a challenging problem. In this paper, we examine the pix2pix network to fit for the reconstruction of realistic high-quality fingerprint images from minutiae maps. For encoding minutiae in minutiae maps we propose directed line and pointing minutiae approaches. We extend the pix2pix architecture to process complete plain fingerprints at their native resolution. Although our focus is on biometric fingerprints, the same concept fits for synthesis of latent fingerprints. We train models based on real and synthetic datasets and compare their performances regarding realistic appearance of generated fingerprints and reconstruction success. Our experiments establish pix2pix to be a valid and scalable solution. Reconstruction from minutiae enables identity-aware generation of synthetic fingerprints which in turn enables compilation of large-scale privacy-friendly synthetic fingerprint datasets including mated impressions.