A Hidden Markov Model For Iris Recognition Method
Tong Wang, HE Pi-lian · 2007
Iris identification system is mainly composed of iris image acquisition, iris image preprocessing and iris image matching. Iris image matching is the key step to the system and effects on the precision and efficiency of the whole system directly. Iris image matching is mainly based on its texture, which can be present by the orientation field. An iris, which has the different orientation angle structure in different area and has a texture pattern correlation with the neighborhood areas, can be viewed as a Markov stochastic field. In this paper, a novel method based on Hidden Markov model that is used to model the orientation field of irises is present. The accurate and robust iris image matching can be achieved by matching Hidden Markov model parameters, which are produced and trained after the processing of extracting eigenirises to form the samples of observation vectors.