Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: Editor-in-chief: Minhua Ma, Patricia Goncalves
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Journal of Computer Sciences and Applications. 2013, 1(5), 80-84
DOI: 10.12691/jcsa-1-5-1
Open AccessArticle

A New Iris Detection Method based on Cascaded Neural Network

Faezeh Mohseni Moghadam1, Azadeh Ahmadi1, and Farshid Keynia2

1Department of Computer Engineering, University of Science and Technology, Kerman,Iran

2Graduate University of Advanced Technology,Kerman,Iran

Pub. Date: June 18, 2013

Cite this paper:
Faezeh Mohseni Moghadam, Azadeh Ahmadi and Farshid Keynia. A New Iris Detection Method based on Cascaded Neural Network. Journal of Computer Sciences and Applications. 2013; 1(5):80-84. doi: 10.12691/jcsa-1-5-1


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.

biometric Iris localization feedforward neural network cascaded neural network daugman's methodneural network designing

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