access icon free Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints

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-based method 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.

Inspec keywords: fingerprint identification; nonparametric statistics

Other keywords: translation invariance; synthetic fingerprint generation improvement; rotation invariance; SFinGe software; fingerprint recognition algorithm; MH; realness test; fixed-length feature vector; FVC2000 database; discriminative performance evaluation; synthetically generated fingerprint images; Bicz software; second-order extended minutiae histograms; FVC2004 database; FVC2002 database; nonparametric distribution-based method; real fingerprint images; artificial fingerprints

Subjects: Image recognition; Computer vision and image processing techniques

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