In this study, the authors show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. They propose a non-parametric distribution-basedmethod usi...
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In this study, the authors show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. They propose a non-parametric distribution-basedmethod using 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 study, 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. In addition, they evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz against real fingerprint images. They conclude with suggestions for the improvement of synthetic fingerprint generation.
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