  Current face recognition systems robustly recognize identities across a wide
variety of imaging conditions. In these systems recognition is performed via
classification into known identities obtained from supervised identity
annotations. There are two problems with this current paradigm: (1) current
systems are unable to benefit from unlabelled data which may be available in
large quantities; and (2) current systems 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? We propose an integrated Bayesian model
that coherently reasons about the observed images, identities, partial
knowledge about names, and the situational context of each observation. While
our model achieves good recognition performance against known identities, it
can also discover new identities from unsupervised data and learns to associate
identities with different contexts depending on which identities tend to be
observed together. In addition, the proposed semi-supervised component is able
to handle not only acquaintances, whose names are known, but also unlabelled
familiar faces and complete strangers in a unified framework.
