eng Science and Education Publishing Journal of Computer Sciences and Applications 2328-725X 2013-06-18 1 1 80 84 10.12691/jcsa-1-5-1 JCSA2013151 article Faezeh Mohseni Moghadam 1 Azadeh Ahmadi a.ahmadi@iauk.ac.ir 1 Farshid Keynia 2 Department of Computer Engineering, University of Science and Technology, Kerman,Iran Graduate University of Advanced Technology,Kerman,Iran 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. http://pubs.sciepub.com/jcsa/1/5/1/jcsa-1-5-1.pdf biometric Iris localization feedforward neural network cascaded neural network daugman's method neural network designing