Automatic Face Recognition System for Hidden Markov Model Techniques

Hidden Markov Models (HMMs) are a class of statistical models used to characterize the observable properties of a signal. HMMs consist of two interrelated processes: (i) an underlying, unobservable Markov chain with a finite number of states governed by a state transition probability matrix and an initial state probability distribution, and (ii) a set of observations, defined by the observation density functions associated with each state. In this chapter we begin by describing the generalized architecture of an automatic face recognition (AFR) system. Then the role of each functional block within this architecture is discussed. A detailed description of the methods we used to solve the role of each block is given with particular emphasis on how our HMM functions. A core element of this chapter is the practical realization of our face recognition algorithm, derived from EHMM techniques. Experimental results are provided illustrating optimal data and model configurations. This background information should prove helpful to other researchers who wish to explore the potential of HMM based approaches to 2D face and object recognition.

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