This paper presents a comparative study of two well-known classification techniques of iris patterns, along with detailed

description of some preprocessing steps. In preprocessing stage, Circular Hough Transform and Canny Edge
Detector are employed for iris segmentation, while for iris normalization and feature extraction, the Rubber Sheet
Model and one-dimensional (1-D) Log-Gabor Filter are used respectively. Finally for classification/matching of iris
patterns, Hamming Distance and Support Vector Machine (SVM) are applied. The evaluation results on CASIA V.1
dataset show that Hamming distance algorithm is more suitable for the classification (with average accuracy of
93.85 %) of iris patterns.