Separable lattice 2-D HMMS introducing state duration control for recognition of images with various variations

In this paper, an extension of separable lattice HMMs (SL-HMM) is described that introduces state duration control for dealing with images with various variations. SL-HMM are generative models that have size and location invariances based on state transition of HMMs. An extended model that has the structure of hidden semi-Markov models (HSMMs) in which the state duration probability is explicitly modeled by parametric distributions is also proposed. However, in this model, each state duration in a Markov chain is independent. It is supposed that each state duration should have a correlation. Therefore, in this paper, we propose a novel model that solves this problem by introducing variables representing the correlation among the state durations. Face recognition experiments show that the proposed model improved the recognition performance for images with size, locational, and rotational variations.