  Current face recognition systems typically operate via classification into
known identities obtained from supervised identity annotations. There are two
problems with this paradigm: (1) current systems are unable to benefit from
often abundant unlabelled data; and (2) they equate successful recognition with
labelling a given input image. Humans, on the other hand, regularly perform
identification of individuals completely unsupervised, recognising the identity
of someone they have seen before even without being able to name that
individual. How can we go beyond the current classification paradigm towards a
more human understanding of identities? In previous work, we proposed an
integrated Bayesian model that coherently reasons about the observed images,
identities, partial knowledge about names, and the situational context of each
observation. Here, we propose extensions of the contextual component of this
model, enabling unsupervised discovery of an unbounded number of contexts for
improved face recognition.
