Face Recognition Based on Separable Lattice HMMS

In this paper, we propose separable lattice hidden Markov models, in which multiple hidden state sequences interact to model the observation on a lattice. The proposed model can be efficiently applied for modeling images, image sequences, 3-D object models and higher dimensional applications, due to the composite structure of Markov chains which reduces the complexity while retaining good properties for multi-dimensional data. In case of 2-D lattices, the proposed model performs an elastic matching in both horizontal and vertical directions; this makes it possible to model not only invariances to the size and location of an object but also nonlinear warping in each dimension. We present a training algorithm for separable lattice HMMs based on a variational approximation. Moreover, the deterministic annealing EM (DAEM) algorithm was applied to the variational algorithm for separable lattice HMMs. Face recognition experiments on the XM2VTS database show that the proposed model has good properties for face image modeling