Examination of Firearms Review: 2007 to 2010 - Interpol

literature is so abundant in this area that we cannot hope for exhaustiveness here,
but it was felt important to provide some entries dealing with this subject, especially
when the research work proposed has the potential for a change in practice in the
short term.
Matching performance is examined when plain as well as rolled impressions are
used, and different fusion techniques are investigated, at feature, score, and rank
level (79). Boosted max fusion at score level performed best; here, a rank-1
identification rate of 83% was obtained, which compares favourably to the rank-1
identification rates of 57.8% for plain and 70.4% for rolled impressions.
The NIST engaged in collaboration a range of AFIS providers and parties in the
definition of a new exchange format adding an extended set of fingerprint features:
dots, incipient ridges, ridge edge protrusions and pores. All relevant documents are
available on http://fingerprint.nist.gov/standard/cdeffs/. The new standard ANSI/NIST-
ITL 1-2010 (now in draft version 0.4) will replace the ANSI/NIST-ITL 1-2007 and
ANSI/NIST-ITL 2-2008 standards that address the interchange of fingerprint, facial,
and SMT data).
The use of this extended feature set has been the subject of a preliminary report
(77). The participating organizations of this trial are SAGEM, NEC, Cogent, Sonda
and Warwick. The number of marks used was 1114, and here, the mates had not
been found using an AFIS search. The different features of the marks used were:
image only; annotated region of interest (ROI); ROI, pattern class and quality map;
minutiae with ridge counts; extended features; extended features with skeleton; and
minutiae with ridge counts but without the image. Human markup was used for the
marks, while automated feature extraction was used for the 100000 (ten-print)
records used as a background database. When extended features are added,
performance is improved only for a subset of participants.
Kwan and co-workers propose to incorporate expert feedback into AFIS (87).
Relevance feedback is proposed as an add-on module to automated systems. The
examiners decisions (‘positive’, when the impressions are judged similar, or
‘negative’ otherwise) are used to transform the input space. When the examiner is
faced with a list of possible matches, the positive match declared will be used to
decrease the distance (or increase the score) by a certain amount, while a negative
declared will do the opposite. A new comparison will then be mapped onto a
semantic space created on the basis of the transformed distance (or score) matrix; in
this way, the short-listed results will be increasingly similar with respect to the
examiners assessment.
The use of large, computerized databases may lead to the discovery of impressions
that come from different fingers that show more similarity than what was expected
before the use of AFIS searches. This is the subject of an article by Dror and
Mnookin (84). In particular, the authors consider that the way fingerprint conclusions
are arrived at (in particular the sufficiency requirements for an individualisation)
should change with the use of AFIS. They also discuss the possible introduction of
bias through the candidate list and the associated scores. The interaction between
the examiner and the automated system is also the subject of an article by Wertheim
(86). The different necessary steps for a successful search in an AFIS were followed
on a set of 1368 images of marks. These steps were formatting (proper orientation
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