Embedded Bayesian networks for face recognition

The embedded Bayesian networks (EBN) introduced in this paper, are a generalization of the embedded hidden Markov models previously used for face and character recognition. An EBN is defined recursively as a hierarchical structure where the "parent" node is a Bayesian network (BN) that conditions the EBNs or the observation sequence that describes the nodes of the "child" layer. With an EBN, one can model complex N-dimensional data, avoiding the complexity of N-dimensional BNs while still preserving their flexibility and partial scale invariance. In this paper we present an application of the EBNs for face recognition and show the improvement of this approach versus the "eigenface" and the embedded HMM approaches.