  In this work, we address the problem of large-scale online face clustering:
given a continuous stream of unknown faces, create a database grouping the
incoming faces by their identity. The database must be updated every time a new
face arrives. In addition, the solution must be efficient, accurate and
scalable. For this purpose, we present an online gaussian mixture-based
clustering method (OGMC). The key idea of this method is the proposal that an
identity can be represented by more than just one distribution or cluster.
Using feature vectors (f-vectors) extracted from the incoming faces, OGMC
generates clusters that may be connected to others depending on their proximity
and their robustness. Every time a cluster is updated with a new sample, its
connections are also updated. With this approach, we reduce the dependency of
the clustering process on the order and the size of the incoming data and we
are able to deal with complex data distributions. Experimental results show
that the proposed approach outperforms state-of-the-art clustering methods on
large-scale face clustering benchmarks not only in accuracy, but also in
efficiency and scalability.
