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Iris recognition biometric systems apply mathematical pattern recognition techniques to images of the irises of an individual¿s eyes. It plays a vital role in our world because of the features inside iris never changed with the years. A fully, architecture design is made for iris recognition system and tested on a computer using Graphic user interface (GUI) in Matlab. The architecture design of the iris recognition consists of segmentation, normalization, features extraction, and matching. The algorithm that used for segmentation is canny edge detector algorithm and Circle Hough Transform algorithm. While Bresenham Circle Algorithm is used for unwarping iris. Ridge Energy Direction (RED) is used as a way of extracting the iris features, Hamming Distance is used for matching between irises. Field Programmable Gate Array (FPGA) is used to reduce the time execution of iris compare to Central Processing Unit (CPU). A novel method is used in order to reduce execution time by taking a quarter part from the iris region for identification. This novel method gives zero faults in recognition when applied on eye images for two different databases are known as CASIA V1 and CASIA Interval.
| Autorius: | Aqeel Al-Hilali |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2017 |
| Knygos puslapių skaičius: | 164 |
| ISBN-10: | 3330037148 |
| ISBN-13: | 9783330037144 |
| Formatas: | 220 x 150 x 10 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
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