A Review of Hidden Markov Models in Face Recognition
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چکیده
Hidden Markov Models (HMMs) are a set of statistical models used to characterize the statistical properties of a signal. An HMM is a doubly stochastic process with an underlying stochastic process that is not observable, but can be observed through another set of stochastic processes that produce a sequence of observed symbols. An HMM has a finite set of states, each of which is associated with a multidimensional probability distribution; transitions between these states are governed by a set of probabilities. Hidden Markov Models are especially known for their application in 1D pattern recognition such as speech recognition, musical score analysis, and sequencing problems in bioinformatics. More recently they have been applied to more complex 2D problems and this review focuses on their use in the field of automatic face recognition, tracking the evolution of the use of HMMs from the early-1990’s to the present day. Our goal is to enable the interested reader to quickly review and understand the state-of-art for HMM models applied to face recognition problems and to adopt and apply these techniques in their own work.
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تاریخ انتشار 2012