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Development of Artificial Bee Colony Algorithm for Iris Segmentation by Z Zainal Abidin; N A Zakaria; Z Abal Abas; S N A Azizan is a Engineering article available to read on EtoBox.

What is Development of Artificial Bee Colony Algorithm for Iris Segmentation about?

## Abstract Iris segmentation is a crucial process in the iris recognition that it locates and captures the unique features inside the human iris image. However, iris image contains high noise rate and sometimes a genuine user is recognized as non-genuine by computer system. An algorithm from swarm-based intelligence is demanded for natural search images. Therefore, an experimental investigation was conducted to explore artificial bee colony (ABC) algorithm for iris segmentation. The ABC algorithm measures the performance of convergence in learning the authentic iris features. Based on the findings, the average fitness is always less than or equal to the best fitness, and the differences between the two goes on decreasing over time, which is until the algorithm completely converges. The parameters of ABC Algorithm are programmed with at best 100 times of calculation to collect the best iteration and best fitness. In fact, results showed that ABC uses the population for every iteration instead of a single iteration. Number of iterations directly affect the time of optimal search for ideal convergence, which meets the aim of this study. As a conclusion, if the mean and standard devia

Who reads Development of Artificial Bee Colony Algorithm for Iris Segmentation?

Author

Z Zainal Abidin; N A Zakaria; Z Abal Abas; S N A Azizan

Publisher

IOP Publishing (ISSN 1757-8981)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)