Irish recognition has always been an attractive goal for researchers. The identification of the person based on iris recognition is very popular due to the uniqueness of the pattern of iris. Although a number of methods for iris recognition have been proposed by many researchers in the last few years. This paper proposes statistical texture feature based iris matching method for recognition using K-NN classifier. Statistical texture measures such as mean, standard deviation, entropy, skewness etc., and six features are computed of normalized iris image. K-NN classifier matches the input iris with the trained iris images by calculating the Euclidean distance between two irises. The performance of the system is evaluated on 500 iris images, which gives good classification accuracy with reduced FAR/FRR.

This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.