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

One-class classification with application to forensic analysis