  The advent of large scale multi-epoch surveys raises the need for automated
light curve (LC) processing. This is particularly true for eclipsing binaries
(EBs), which form one of the most populated types of variable objects. The Gaia
mission, launched at the end of 2013, is expected to detect of the order of few
million EBs over a 5-year mission.
  We present an automated procedure to characterize EBs based on the geometric
morphology of their LCs with two aims: first to study an ensemble of EBs on a
statistical ground without the need to model the binary system, and second to
enable the automated identification of EBs that display atypical LCs. We model
the folded LC geometry of EBs using up to two Gaussian functions for the
eclipses and a cosine function for any ellipsoidal-like variability that may be
present between the eclipses. The procedure is applied to the OGLE-III data set
of EBs in the Large Magellanic Cloud (LMC) as a proof of concept. The bayesian
information criterion is used to select the best model among models containing
various combinations of those components, as well as to estimate the
significance of the components.
  Based on the two-Gaussian models, EBs with atypical LC geometries are
successfully identified in two diagrams, using the Abbe values of the original
and residual folded LCs, and the reduced $\chi^2$. Cleaning the data set from
the atypical cases and further filtering out LCs that contain non-significant
eclipse candidates, the ensemble of EBs can be studied on a statistical ground
using the two-Gaussian model parameters. For illustration purposes, we present
the distribution of projected eccentricities as a function of orbital period
for the OGLE-III set of EBs in the LMC, as well as the distribution of their
primary versus secondary eclipse widths.
