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We recently introduced a novel approximation of the intractable two-dimensional hidden Markov model (2D HMM), the turbo-HMM (T-HMM), which consists of a set of interconnected horizontal and vertical 1D HMMs. In this paper, we consider the extension of this framework to the continuous state HMM, generally referred to as the state-space model (SSM). We provide efficient approximate answers to the three following problems: (1) how to compute the likelihood of a set of observations; (2) how to find the sequence of states that best "explains" a set of observations; and (3) how to estimate the model parameters given a set of observations. The application of this work to the challenging problem of face recognition, in the presence of large illumination variations, illustrates the potential of our approach.
Date of Conference: 17-21 May 2004