Propagation of facial identities in a social network

We address the problem of automated face recognition on a social network using a loopy belief propagation framework. The proposed approach propagates the identities of faces in photos across social graphs. We characterize performance in terms of structural properties of a social network. This is accomplished by conducting extensive simulations on synthetic networks. We propose a distance metric defined using face recognition results for detecting hidden connections. The result demonstrates that the constraints imposed by the social network have the potential to improve the performance of face recognition methods. The result also shows it is possible to discover hidden connections in a social network based on face recognition.