Open Access

ARTICLE

Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

by Slim Ben Chaabane^1,2,*, Rafika Harrabi^1,2, Anas Bushnag¹, Hassene Seddik²

1 Computer Engineering Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, 47512, Saudi Arabia
2 Department of Electrical Engineering, University of Tunis, CEREP, ENSIT 5 Av, Taha Hussein, 1008, Tunis, Tunisia

* Corresponding Author: Slim Ben Chaabane. Email:

Journal on Artificial Intelligence 2022, 4(4), 201-214. https://doi.org/10.32604/jai.2022.032850

Received 31 May 2022; Accepted 28 July 2022; Issue published 25 May 2023

Abstract

Biometrics represents the technology for measuring the characteristics of the human body. Biometric authentication currently allows for secure, easy, and fast access by recognizing a person based on facial, voice, and fingerprint traits. Iris authentication is one of the essential biometric methods for identifying a person. This authentication type has become popular in research and practical applications. Unlike the face and hands, the iris is an internal organ, protected and therefore less likely to be damaged. However, the number of helpful information collected from the iris is much greater than the other biometric human organs. This work proposes a new iris identification model based on a multilevel thresholding technique and modified Fuzzy c-means algorithm. The multilevel thresholding technique extracts the iris from its surroundings, such as specular reflections, eyelashes, pupils, and sclera. On the other hand, the modified Fuzzy c-means is used to combine and classify the most useful statistical features to maximize the accuracy of the collected information. Therefore, having the most optimal iris recognition. The proposed model results are validated using True Success Rate (TSR) and compared to other existing models. The results show how effective the combination of the two stages of the proposed model is: the Otsu method and modified Fuzzy c-means for the 400 tested images representing 40 people.

---

Keywords

---