A Novel and Efficient Method to Extract Features and Vector Creation in Iris Recognition System

The selection of the optimal feature subset and the classification has become an important issue in the field of iris recognition. In this paper we propose several methods for it-is feature Subset selection and vector creation. In this paper we propose a new feature extraction method for iris recognition based Oil contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. For reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last. the feature vector is created by two methods: co-occurrence matrix properties and contourlet coefficients. For analyzing the desired performance of our proposed method, we use the CASIA dataset, which is comprised of 108 classes with 7 images in cacti class and each class represented a person. Experimental results show that the proposed increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods.