shape adaptive, robust iris feature extraction from noisy iris images

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extractedfrom there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts mayinfluence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been consideredin the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor wavelet forfeature extraction on the iris recognition performance. In addition, an effective noise removing approach is proposed in this paper.The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decisionmaking. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden throughomitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse?to?fine strategy. The principle of mask codegeneration is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimentalresult shows that by using the shape adaptive Gabor wavelet technique there is an improvement on the accuracy of recognition rate.