ABSTRACT
Swarm intelligence algorithm is inspired by a collective behaviour from a group of social insects. A population of insects is represented as one particle and moves through the space in emergence and in a huge size. The artificial insect explores global optimal solution and based on social behaviour for searching for food. Searching is more natural and for the first time of initiation, the learning process is consuming a lot of computational speed and memory space usage. Once, the artificial ant or insect learned about the iris feature on the template, the artificial ant recognized faster for second and third time.The artificial ant tolerates with high noise, meaning it adapts with small changes in iris template as long as the main location point of the iris features are still at the same point in the region of interest area. However, element of control and proper termination function need to be implemented for swarm-based iris recognition since the endless journey to search for the optimal solution. A combination of several algorithms with swarm intelligent brings a mechanism that is more lightweight, faster and provide a security to the iris template.
