  Iris recognition is considered as one of the best biometric methods used for
human identification and verification, this is because of its unique features
that differ from one person to another, and its importance in the security
field. This paper proposes an algorithm for iris recognition and classification
using a system based on Local Binary Pattern and histogram properties as a
statistical approaches for feature extraction, and Combined Learning Vector
Quantization Classifier as Neural Network approach for classification, in order
to build a hybrid model depends on both features. The localization and
segmentation techniques are presented using both Canny edge detection and Hough
Circular Transform in order to isolate an iris from the whole eye image and for
noise detection .Feature vectors results from LBP is applied to a Combined LVQ
classifier with different classes to determine the minimum acceptable
performance, and the result is based on majority voting among several LVQ
classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different
extensions and size are presented. Since LBP is working on a grayscale level so
colored iris images should be transformed into a grayscale level. The proposed
system gives a high recognition rate 99.87 % on different iris datasets
compared with other methods.
