From Face Recognition to Models of Identity:A Bayesian Approach to Learning aboutUnknown Identities from Unsupervised Data
Abstract. 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.