  Fingerprints feature a ridge pattern with moderately varying ridge frequency
(RF), following an orientation field (OF), which usually features some
singularities. Additionally at some points, called minutiae, ridge lines end or
fork and this point pattern is usually used for fingerprint identification and
authentication. Whenever the OF features divergent ridge lines (e.g. near
singularities), a nearly constant RF necessitates the generation of more ridge
lines, originating at minutiae. We call these the necessary minutiae. It turns
out that fingerprints feature additional minutiae which occur at rather
arbitrary locations. We call these the random minutiae or, since they may
convey fingerprint individuality beyond the OF, the characteristic minutiae. In
consequence, the minutiae point pattern is assumed to be a realization of the
superposition of two stochastic point processes: a Strauss point process (whose
activity function is given by the divergence field) with an additional hard
core, and a homogeneous Poisson point process, modelling the necessary and the
characteristic minutiae, respectively. We perform Bayesian inference using an
MCMC-based minutiae separating algorithm (MiSeal). In simulations, it provides
good mixing and good estimation of underlying parameters. In application to
fingerprints, we can separate the two minutiae patterns and verify by example
of two different prints with similar OF that characteristic minutiae convey
fingerprint individuality.
