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A Neural Network Based Iris Recognition System for Personal Identification


Affiliations
1 Department of Electronics and Telecommunication, Padre Conceicao College of Engineering, Goa, India
     

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This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person's left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

Keywords

Biometric, Iiris Recognition, Artificial Neural Network.
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  • A Neural Network Based Iris Recognition System for Personal Identification

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Authors

Usham Dias
Department of Electronics and Telecommunication, Padre Conceicao College of Engineering, Goa, India
Vinita Frietas
Department of Electronics and Telecommunication, Padre Conceicao College of Engineering, Goa, India
P. S. Sandeep
Department of Electronics and Telecommunication, Padre Conceicao College of Engineering, Goa, India
Amanda Fernandes
Department of Electronics and Telecommunication, Padre Conceicao College of Engineering, Goa, India

Abstract


This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person's left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

Keywords


Biometric, Iiris Recognition, Artificial Neural Network.