Image recognition based on separable lattice trajectory 2-D HMMS

In this paper, a novel statistical model for image recognition based on separable lattice 2-D HMMs (SL2D-HMMs) is proposed. Although SL2D-HMMs can model invariance to size and location deformation, its modeling accuracy is still insufficient because of the following two assumptions: i) the statistics of each state are constant and ii) the state output probabilities are conditionally independent. In this paper, SL2D-HMMs are reformulated as a trajectory model that can capture dependencies between adjacent observations. The effectiveness of the proposed model was demonstrated in face recognition and image alignment experiments.