  Iris Recognition Systems are ocular- based biometric devices used primarily
for security reasons. The complexity and the randomness of the Iris, amongst
various other factors, ensure that this biometric system is inarguably an exact
and reliable method of identification. The algorithm is responsible for
automatic localization and segmentation of boundaries using circular Hough
Transform, noise reductions, image enhancement and feature extraction across
numerous distinct images present in the database. This paper delves into the
various kinds of techniques required to approximate the pupillary and limbic
boundaries of the enrolled iris image, captured using a suitable image
acquisition device and perform feature extraction on the normalized iris image
with the help of Haar Wavelets to encode the input data into a binary string
format. These techniques were validated using images from the CASIA database,
and various other procedures were also tried and tested.
