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 deviation is low in values, it is proven that the speed of searching the iris features is faster and more robust.
Export citation and abstract BibTeX RIS
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.