الفهرس 
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Abstract
With the increasing demands of security in our daily life, the systems for person
recognition based on biometric features have broad applications in both commercial and
security areas. In addition to that, there is a rapid increase in the need of accurate and
reliable personal identification infrastructure in recent years. So, Iris recognition has been
regarded as one of the most reliable biometric technologies in recent years and iris has
distinct advantages. Since the degree of freedom of iris textures is extremely high, the
probability of finding two identical irises is close to zero, therefore, the iris recognition
systems are very reliable and could be used in most secure places. which can be used for
differentiating the individuals. A human iris has an extraordinary amount of unique details
which identify and characterize the person. So, In order to use the iris pattern for
identification, it is important to define a representation that well adapted for extracting the
iris information content from images of the human eye. So, to extract useful information
from the iris image that contains the unique details that characterize the person and which
are using in the classification for verifying the identity of the person, need to construct a
system has high accuracy and efficiency, to meet this demand. In this thesis, present GA-
SVM system which consists of two step. In first step integrate Genetic Algorithms with
Independent Component Analysis to extract feature vector for each iris image and then
Integration of genetic algorithms with Support Vector Machine in order to do
classification in second step.