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Title: An effective segmentation method for iris recognition based on fuzzy logic using visible feature points
Authors: Nachar, Rabih 
Inaty, Elie 
Affiliations: Issam Fares Faculty of Technology 
Department of Computer Engineering 
Keywords: Artificial intelligence
Feature points
Fuzzy logic
iris features
iris recognition
iris segmentation
Issue Date: 2022-03
Publisher: Springer
Part of: Multimedia tools and applications
Volume: 81
Issue: 7
Start page: 9803
End page: 9828
In this paper, the edge corners (ECs) are proposed as new visible feature points located at the edges of visible iris features such as crypts, pigment spots and stripes. A new technique is developed to segment the iris using the ECs. In addition, an efficient artificial intelligence based fuzzy logic system for the iris recognition stage is used to mitigate the randomness of the iris’s visible features due to pupil dilations and elastic distortions. Iris recognition is achieved by comparing the distribution pattern of the ECs using the proposed fuzzy logic system with four linguistic variables. The first goal is to achieve a high recognition rate with very low computational time. The second goal is to facilitate the use of iris recognition in forensics by using only ECs of the visible features of the iris rather than using full images of those features. Therefore, the proposed fuzzy logic based iris segmentation and recognition (FLISR) system can be used for automatic evaluation and manual verification. In the automatic evaluation, the system finds the best gallery iris image(s) matching the input probe image. Manual verification is done when trained examiners perform independent inspections to determine the best matching iris image. Extensive experiments with different data sets demonstrate the efficiency of the proposed FLISR. In terms of iris segmentation, the iris localization has reached an average accuracy of 99.85%. In addition, the average matching accuracy of the iris recognition has achieved 99.83% with very low computational time as compared to similar algorithms available in the literature.
ISSN: 13807501
DOI: 10.1007/s11042-022-12204-8
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Telecommunications and Networking Engineering
Department of Computer Engineering

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