Danlami, Muktar and Ramli, Sofia Najwa and Jemain, Nur Izzah Syahira and Pindar, Zahraddeen and Jamel, Sapiee and Deris, Mustafa Mat (2018) An efficient iris image thresholding based on binarization threshold in black hole search method. International Journal of Engineering and Technology, 7 (4.31). pp. 34-39. ISSN 2227-524X
Full text not available from this repository. (Request a copy)Abstract
In iris recognition system, the segmentation stage is one of the most important stages where the iris is located and then further segmented into outer and lower boundary of iris region. Several algorithms have been proposed in order to segment the outer and lower boundary of the iris region. The aim of this research is to identify the suitable threshold value in order to locate the outer and lower boundaries using Black Hole Search Method. We chose these methods because of the ineffient features of the other methods in image indetification and verifications. The experiment was conducted using three data set; UBIRIS, CASIA and MMU because of their superiority over others. Given that different iris databases have different file formats and quality, the images used for this work are jpeg and bmp. Based on the experimentation, most suitable threshold values for identification of iris aboundaries for different iris databases have been identified. It is therefore compared with the other methods used by other researchers and found out that the values of 0.3, 0.4 and 0.1 for database UBIRIS, CASIA and MMU respectively are more accurate and comprehensive. The study concludes that threshold values vary depending on the database.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Iri; Image processing; Black Hole Search Method; Segmentation |
| Subjects: | H Social Sciences > HT Communities. Classes. Races > HT101-395 Urban groups. The city. Urban sociology H Social Sciences > HT Communities. Classes. Races > HT101-395 Urban groups. The city. Urban sociology > HT165.5-169.9 City planning T Technology > T Technology (General) > T11.95-12.5 Industrial directories > T58.5-58.64 Information technology |
| Divisions: | Faculty of Computer Science and Information Technology > Department of Information Security Faculty of Computer Science and Information Technology > Department of Web Technology |
| Depositing User: | UiTM Student Praktikal |
| Date Deposited: | 07 Dec 2021 04:53 |
| Last Modified: | 07 Dec 2021 04:53 |
| URI: | http://eprints.uthm.edu.my/id/eprint/4522 |
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