Separating the Real from the Synthetic: Minutiae Histograms as Fingerprints of Fingerprints
This addresses the need for reliable authentication in biometric systems by detecting synthetic fingerprints, though it is incremental as it builds on existing minutiae-based methods.
The study tackled the problem of distinguishing synthetically generated fingerprints from real ones, achieving very high accuracy using second order extended minutiae histograms across multiple benchmark databases including FVC2000, FVC2002, and FVC2004.
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.