Version 1
: Received: 22 September 2024 / Approved: 23 September 2024 / Online: 24 September 2024 (03:51:26 CEST)
How to cite:
Ben Chaabane, S.; Harrabi, R. M. S.; Seddik, H. Iris Recognition System using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robot Control. Preprints2024, 2024091698. https://doi.org/10.20944/preprints202409.1698.v1
Ben Chaabane, S.; Harrabi, R. M. S.; Seddik, H. Iris Recognition System using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robot Control. Preprints 2024, 2024091698. https://doi.org/10.20944/preprints202409.1698.v1
Ben Chaabane, S.; Harrabi, R. M. S.; Seddik, H. Iris Recognition System using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robot Control. Preprints2024, 2024091698. https://doi.org/10.20944/preprints202409.1698.v1
APA Style
Ben Chaabane, S., Harrabi, R. M. S., & Seddik, H. (2024). Iris Recognition System using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robot Control. Preprints. https://doi.org/10.20944/preprints202409.1698.v1
Chicago/Turabian Style
Ben Chaabane, S., Rafika Mohamed Salah Harrabi and Hassene Seddik. 2024 "Iris Recognition System using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robot Control" Preprints. https://doi.org/10.20944/preprints202409.1698.v1
Abstract
The idea of developing a robot controlled by iris movement to assist physically disabled individuals is indeed innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has huge impact on the life of physically disabled people. Therefore, there is need to develop a robot which can be controlled using iris movement.
The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid's position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities.
The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. The fast gradient filters using a fuzzy inference system (FIS) is employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the Fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques.
The results of the proposed model are validated using True Success Rate (TSR) and compared to other existing models. The results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.
Keywords
iris recognition; bald eagle search algorithm; fast gradient filters; PCA; FKNN; fuzzy inference system (FIS); TSR; sensitivity; classification; Robot control
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
