Topological dynamic Bayesian networks: Application to human face identification across ages

One of the major restrictions of dynamic Bayesian networks (DBNs) is their inability to account for topological features such as shape descriptors, homeomorphy, homotopy, and invariance. The main reason for this shortcoming is explained by the fact that even if dynamic Bayesian networks encode statistical relationships; they are not embedded in a Euclidean space where mathematical structures abound. The goal is to embed DBNs into a Euclidean space such that these topological features can be exploited. This extension of DBNs to topological DBNs (TDBNs) leapfrogs the task of pattern recognition and machine learning by not only classifying objects but revealing how they are related topologically. We have applied the TDBN formalism to facial aging for person identification. Preliminary results reveal that the TDBNs outperform the traditional DBN with an accuracy margin of 8% in average.