  The analysis of broken glass is forensically important to reconstruct the
events of a criminal act. In particular, the comparison between the glass
fragments found on a suspect (recovered cases) and those collected on the crime
scene (control cases) may help the police to correctly identify the
offender(s). The forensic issue can be framed as a one-class classification
problem. One-class classification is a recently emerging and special
classification task, where only one class is fully known (the so-called target
class), while information on the others is completely missing. We propose to
consider classic Gini's transvariation probability as a measure of typicality,
i.e. a measure of resemblance between an observation and a set of well-known
objects (the control cases). The aim of the proposed Transvariation-based
One-Class Classifier (TOCC) is to identify the best boundary around the target
class, that is, to recognise as many target objects as possible while rejecting
all those deviating from this class.
