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American Journal of Business and Operations Research
Volume 1 , Issue 1, PP: 19-27 , 2020 | Cite this article as | XML | Html |PDF


Fuzzy Clustering and Classification based Iris Recognition: A Medical Application

Authors Names :   K. Shankar   1 *  

1  Affiliation :  Department of Computer Applications, Alagappa University, Karaikudi, India

    Email :  shankarcrypto@gmail.com

Doi   :   https://doi.org/10.54216/AJBOR.010102

Received February 02, 2019 Accepted August 30, 2019

Abstract :

The Iris based authentication framework is basically a pattern recognition strategy that utilizes iris designs, which are factually unique. In this study, an efficient iris recognition system is developed with the help of Possibilistic Fuzzy C-Means Clustering (PFCM) and Fuzzy Logic classifier (FLC). The proposed methodology consists of four modules namely, pre-processing, segmentation, normalization and recognition. Initially, in the pre-processing module, the input images are adjusted to do further processing. Then, we segment the iris region from the input image with the help of PFCM. After that, the segmented image is normalized with the help of the Daugman Robber Sheet Model (DRS). Finally, the iris image is recognized with the help of FLC. The performance of the proposed methodology is analyzed in terms of different metrics namely, accuracy, false acceptance ratio (FAR) and false rejection ratio (FRR). Experimental results demonstrate, proposed PFCM+FLC method attains a better accuracy of 97.5%

Keywords :

Possibilistic fuzzy c-means clustering , fuzzy logic classifier , iris recognition , segmentation , and authentication.

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Cite this Article as :
K. Shankar, Fuzzy Clustering and Classification based Iris Recognition: A Medical Application, American Journal of Business and Operations Research, Vol. 1 , No. 1 , (2020) : 19-27 (Doi   :  https://doi.org/10.54216/AJBOR.010102)