Optimization Technique for Improving Iris Recognition System

Over the years it has been known that the iris patterns have a wonderful and rich structure and are full of complex textures in contrast to other physiological characteristics. Iris is the inner organ of the human body that is directly band easily visible with the nacked eye. Because of its special physiology architecture, the iris becomes very sensitive to the status of all other organs.IRIS recognition is a particular type of biometric system that can be used to reliably identify a person by analyzing the patterns found in the IRIS. Many techniques have been developed for iris recognition. As an open and demanding problem, accurate modeling of polarization curve in proton exchange membrane fuel cell has become the main issue of various researches. In recent years, because of their great potentials, metaheuristic optimization algorithms have represented good performances in identification of the unknown parameters of the proton exchange membrane fuel cell model, but there is the possibility to obtain more accurate results with more capable algorithms. In the literature, many heuristic optimization algorithms have been developed on the basis of natural phenomena. However, there are still some possibilities to devise new ones. In this paper, evolution of bird species has been regarded, and the intelligent behavior of birds during mating season has become an inspiration to devise a new heuristic optimization algorithm, named bird mating optimizer.

 we have implemented the BMO(Bird mating optimization) to improve the accuracy of an iris recognition system. This was a research project as it was never implemented and integrated with iris system. PSO (Particle swarm optimization)was also implemented & integrated with iris recognition system and it gave 96% of accuracy. BMO as per the research provides more accuracy than Ant colony optimization/Particle swarm optimization or any Genetic algorithm.