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Quadratic differentials naturally define analytic orientation fields on planar surfaces. We propose to model orientation fields of fingerprints by specifying quadratic differentials. Models for all fingerprint classes such as arches, loops and whorls are laid out. These models are parametrised by few, geometrically interpretable parameters which are invariant under Euclidean motions. We demonstrate their ability in adapting to given, observed orientation fields, and we compare them to existing models using the fingerprint images of the NIST Special Database 4. We also illustrate that these model allow for extrapolation into unobserved regions. This goes beyond the scope of earlier models for the orientation field as those are restricted to the observed planar fingerprint region. Within the framework of quadratic differentials we are able to verify analytically Penrose's formula for the singularities on a palm [L. S. Penrose, "Dermatoglyphics"' Scientific American, vol. 221, no.~6, pp. 73--84, 1969]. Potential applications of these models are the use of their parameters as indices of large fingerprint databases, as well as the definition of intrinsic coordinates for single fingerprint images.
Geometric, Pattern analysis, Applications, Smoothing, Fingerprint recognition, orientation field, fingerprint modelling, quadratic differential, rational functions
Axel Munk, Thomas Hotz, Stephan Huckemann, "Global Models for the Orientation Field of Fingerprints: An Approach Based on Quadratic Differentials", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1507-1519, September 2008, doi:10.1109/TPAMI.2007.70826
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