  We present in this article the model Function-described graph (FDG), which is
a type of compact representation of a set of attributed graphs (AGs) that
borrow from Random Graphs the capability of probabilistic modelling of
structural and attribute information. We define the FDGs, their features and
two distance measures between AGs (unclassified patterns) and FDGs (models or
classes) and we also explain an efficient matching algorithm. Two applications
of FDGs are presented: in the former, FDGs are used for modelling and matching
3D-objects described by multiple views, whereas in the latter, they are used
for representing and recognising human faces, described also by several views.
