Human iris recognition system based on D-LDA and nearest feature classifiers
Lang Yan-feng · Jisuanji gongcheng yu sheji · 2006
A human iris recognition system with a high recognition rate is presented.The iris recognition system consists of three major processing phases.First,images of human's eyes from a web camera is captured,and iris images from them is obtained.We further manipulate the iris images using digital image processing techniques,so that the resulting iris images are suited to recognition.Second,the feature vectors from the iris images is made.Before extraction of feature vectors,we must unwrap the iris images.In this phase,the problem of rotation invariant is solved.We then adopt direct linear discriminant analysis to extract feature vectors such that the distance between the feature vectors of different classes is the largest but the distance between those in the same class is the smallest.Finally,the nearest feature classifiers to discriminate the feature vectors is employed.To verify the effectiveness of the proposed methods,we realize a human iris recognition system.The experimental results show that the recognition rate achieves 96.47 % in the case of fewer sampling feature vectors,whereas it can attain 98.50 % if more sampling feature vectors are added to each class.