Examination of Firearms Review: 2007 to 2010 - Interpol

fingerprint, FVC2002 DB1), and the model is used to compute a probability of
random correspondence given the number of minutiae and ridge points in the
reference and query images, as well as the number or correspondences between the
two. This model is further described by Su and Srihari (118) : a mixture of bivariate
Gaussian distributions is used for minutiae location, von Mises distributions are used
for minutiae orientation and the ridge lengths are considered to be distributed
uniformly. Ridge points are described by the distance from the minutia, the direction
between the minutia and the ridge point as well as the orientation of the ridge point.
The distance of ridge points to minutiae is modelled using a one-dimensional
Gaussian distribution. The model, with a computation of specific random
correspondence probabilities, is the subject of (119).
Chen and Moon carry out an investigation of discriminative power using minutiae
data (location, direction). Minutiae location is modelled using a uniform (spatial)
distribution, and a Von Mises distribution is used for the differences between
orientations of two compared minutiae. First, the model for locations is compared to
observed values (obtained on different available fingerprint databases), and second
the model for both locations and directions is compared to known nonmatchcomparisons.
The ‘score’ is constituted of matching pairs of minutiae between the
(nonmatching) impressions. Good correspondence between the model and
observations are shown (121). However, in a following paper, the authors show that
the location of minutiae is not distributed according to complete spatial randomness,
invalidating this first model (122), and the authors propose to model the minutiae
locations using a quantitative stochastic model. The results obtained are again
compared to observed data as well as to the first model, and an improvement (better
correspondence to empirical data) is observed.
The neighbourhood of minutiae is analysed by Hsu and Martin. The locations of
minutiae are shown to be non-random (a preference of four inter-ridge distances is
observed). The relative minutiae orientations and positions are distributed nonisotropically.
Probabilities for various numbers of minutiae and numbers of nearest
neighbours are given. For example, the probability for 12 minutiae is inferior to 10 -30 .
The authors also show that manually annotated minutiae yield results that differ from
those obtained on automatically extracted minutiae (120).
Ridge points are described in (125), and their use for improving fingerprint matching
is assessed using a modified version of the Bozorth and k-minutiae matchers.
Minutiae types and orientations, as well as ridge types (defined using the ridge points
described in (125)) are used in order to compute a PRC (probability of random
correspondence) for fingerprints by Fang and co-workers (126).
Two studies using AFIS (Automated Fingerprint Identification System) in the context
of fingerprint identification have been carried out. One makes use of AFIS scores to
determine the validity of fingerprint identification (115). Comparing mated inked
prints, and using a logistic regression classifier, a threshold is used to declare a
match. When this threshold is set to a probability of 0.5, the classification error is of
2.4 ± 0.1 %. When marks acquired by research assistants were used in a second
experiment, this classification error was of 4.5 ± 0.4%. In the third experiment, where
marks from a NIST database were used (NIST Special Database 27), a classification
error of 5.4 ± 1.0% was obtained (115).
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