Iris Recognition System And Study Of SVM Algorithm

Today, biometirc recognition is a common and reliable way to authenticate the identity of a living person based on physiological or behavioral characteristics. The biomedical literature suggests that similar to fingerprints, irises possess distinct feature for uniquely identifying a person. Furthermore, iris is located on the naked part of human body as to enable the remote examination in the aid of a machine vision system. For this reason, Iris-Identification is regard as a kind of noninvasive human identification technique. This paper elucidates the principles and the typical structure of Iris-Identification System and study SVM theory to classify.A personal identification system based on iris pattern is composed of iris image acquisition, image Preprocessing, feature extraction and matching. Iris Image for this paper is provided by Institute of Automation Chinese Academy of Sciences. Iris feature extraction is based on texture analysis using multi-channel Gabor-wavelet filtering. Compared with existing methods, our method employs the rich 2-D information of the iris translation, rotation, and scale invariant.This article introduced the training algorithm for the newest branch of statistic learning theory-SVM(Support Vector Machine). All the important algorithms' advantages and disadvantages are analyzed. This paper gives some improvements to Platt's SMO algorithm for SVM classifier design. The modified algorithm performs significantly faster than the original SMO on all benchmark datasets tried.