Separating the Real from the Synthetic: Minutiae Histograms as Fingerprints of Fingerprints

TL;DR

This paper introduces a method using minutiae histograms to accurately distinguish real fingerprints from synthetic ones, aiding in the evaluation and improvement of fingerprint generation techniques.

Contribution

The study presents a novel, invariant feature-based approach for reliably discriminating real from synthetic fingerprints across multiple benchmark datasets.

Findings

High accuracy in real vs. synthetic fingerprint discrimination

Effective across various fingerprint databases and generation methods

Provides insights for improving synthetic fingerprint generation

Abstract

In this study we show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. We propose a method based on second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This 'test of realness' can be applied to synthetic fingerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz versus real fingerprint images. We conclude with suggestions for the improvement of synthetic fingerprint generation.