Examination of Firearms Review: 2007 to 2010 - Interpol

A doctoral thesis (131) investigates additional features for use in fingerprint
comparison, including ridge skeletons, pores, dots and incipient ridges. The matching
performance of algorithms is improved by the addition of these features. When
matching is performed in mark to inked print tasks (at 500 dpi) the improvement is
largest for the worst images, and for ridge skeletons. These effects are attributed to
the resolution of the images, which is small for the extraction of pores, in particular.
When the comparisons are carried out between livescans, the improvement is
greatest for the smallest areas used. In a second part, a fingerprint individuality
model is proposed, incorporating minutiae locations and directions, ridge period and
curvature as well as pore spacing. This model is an extension of one of the models
presented above (123). The current model is fitted to five major fingerprint classes
(whorl, left and right loop, arch and tented arch). Minutiae are clustered according to
position and orientation, then position is modelled using a bivariate Gaussian
distribution for each cluster, and the orientation by a Von-Mises distribution. The
ridge period is modelled using a Gaussian mixture, and ridge curvature is modelled
using a Poisson mixture. Finally, pore spacings along the ridge are modelled using a
Gaussian distribution. Estimation of the model parameters follows, and the model is
validated against empirical data. In the last part of the thesis, a touchless 3D
fingerprint acquisition device is presented. In particular, the interoperability between
these 3D images with legacy rolled prints is achieved through an algorithm
constructing rolled-equivalent images from the 3D representations acquired by the
sensor, and another to enhance the acquired images. The work carried out during
this thesis is also the object of several articles (132-134).
The usefulness of integrating level III features into the matching process is also
shown by Vatsa et al. (147), Zhao et al. (148, 149) and Jain et al. (135). In this last
reference, ridge edges as well as pores are used, in a hierarchical approach (level II
is matched, and then level III features are used). Matching results are improved using
this approach, also for images classified as being of low quality. But Indovina and
Hicklin (cited above, under Automated Systems) only find an improvement for some
of the systems tested, therefore highlighting that level III is not always useful in the
matching stage (77).
Pore area reproducibility has been measured on marks developed using ninhydrin
and cyanoacrylate. Ten marks developed using ninhydrin and 50 marks developed
using cyanoacrylate were used. The results show that pore area is not reproducible
(138), indeed, the % coefficient of variance is between 38.5% of the mean and
81.9%.
Distortion
The deformation of the skin under distortion conditions is observed experimentally by
Maceo (139). First, the anatomy of the hand is described in a detailed fashion,
describing the elements that are relevant to distortion. Then, the effects of vertical
and horizontal sheering stresses and torque are examined, first on stills from videos
and second on developed impressions. The two fingers used present different
general patterns (loop and whorl) and differences are analysed qualitatively. This
article is richly illustrated with commented images. A model for skin distortion in a
biometric setting is proposed by Maltoni and Cappelli (140) (as well as its use for
generating fingerprints and a discussion of the use of distortion to detect fake
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