Performance & Analysis of Effective Iris Recognition System Using Independent Component Analysis

To remove artifacts, two post processing techniques that carry out optimization in the Fourier domain are developed. Decompressed iris images obtained from two public iris image databases are evaluated by visual comparison, two objective image quality assessment metrics, and eight iris recognition methods. To improve the efficiency, sensitivity and reduce the complexity. In existing system use the Principal component analysis will work with different parameters in the image in sequence manner and independent component analysis will work with different parameters in the image in same time, but the output is not reliable for a large set of images, in neural network, for each and every time, the large set of feature for image database get loaded for training process, it will increase the time complexity of the whole system. In this proposed system we use the ICA (Independent Component analysis) and Gabor filter to improve the sensitivity, specificity and reducing time complexity in the existing system. The concept of Gabor filter will analysis the input image in several phases and pick a better one through 500 iterations. A new approach for personal identification based on iris recognition is presented in this paper. The core of this paper details the steps of iris recognition, including image processing, feature extraction and classifier design. This paper is implemented using MATLAB