Examination of Firearms Review: 2007 to 2010 - Interpol

These automatic methods will be based on a probabilistic approach where relevant
criteria have to be chosen. That is the most difficult step since handwriting is an evolving
biometry where relevant criteria may be different from one writer to another.
Marquis et al [57] worked on the Bayesian evaluation of the shape of the handwritten
letters “a”. The aim is to provide a quantitative support to handwriting evidence
evaluation. They use Fourier Descriptors to characterise the shape of loops “a”. The
findings demonstrate that the probabilistic methodology correctly supports either the
hypothesis of authorship or the alternative hypothesis.
Srihari et al [24] showed that an automatic handwriting verification system can be a
good tool to demonstrate the individuality of handwriting, through research using the
CEDAR FOX system. He demonstrated that handwriting of twins is less discriminable
than that of non-twins (an overall error rate of 12.9% for twins and 3.7% for non-twins)
and that error rates with identical twins are higher than with fraternal twins. This kind of
use of automation can be considered as a step towards a more scientific approach to
the field of handwriting examination.
Durina et al [29], [41] conducted a research project responding to criticisms that earlier
studies on the individuality of handwriting did not include populations from
“homogeneous writing communities”, and relied on computer analysis of handwriting
rather than on human examiners. The results of their study offered evidence to support
that there is a high degree of inter-writer variation among writers, even in a population of
writers who all were taught the same copybook style at the same elementary school by
the same teachers about 40 years ago, and that FHE were able to extract features from
the writing samples that enable them to attribute authorship.
Walch et al [16], [18], [31], [36], [37], [39] made different papers and presentation related
to a language independent system for handwriting identification named “Flash ID”. The
process is based on writing segmentation (graphemes) that make the system language
independent. The results are presented as excellent and the tool as a good support for
FHEs.
Brink et al [46] worked on the quantity of handwritten text needed for text independent
writer verification and identification. For the best features, it appears that the minimum
amount of text required is about 100 characters. To get this result, they used two
handwriting datasets, Firemaker and IAM.
Blankers et al [51] organised a competition between different automated biometric
signature verification methods using on- and off-line skilled forgery detection. The
results are promising, especially for the on-line process. It also shows that automated
methods might be able to support FHEs.
13. Indented impression
There are generally few papers dealing with the issue of indented impression, however,
we found 7 references. Some were about methods to improve the actual one. For
359