期刊名称：Journal of Computer Sciences and Applications
印刷版ISSN：2328-7268
电子版ISSN：2328-725X
出版年度：2013
卷号：1
期号：5
页码：80-84
DOI：10.12691/jcsa-1-5-1
语种：English
出版社：Science and Education Publishing
摘要：Iris recognition is one of the most reliable and applicable methods for a person's identification. The most complex and important phase of recognition is iris segmentation of an input eye image that affects iris recognition successful rate significantly. Due to missed parameters in noisy images, main error occurs in the performance of classic localization. Artificial neural networks (ANN) are appropriate substitutes for classic methods because of their flexibility on noisy images. In this paper, we use feedforward neural network (FFNN) for the improvement of iris localization accuracy. We apply two methods in order to reduce neural network error: first, designing one neural network for each output neuron .Second, using cascaded feedforward neural network (CFFNN). Then, we examine proposed methods on different datasets which cause remarkable reduction of localization error.
