  Biometrics is the science of identifying an individual based on their
intrinsic anatomical or behavioural characteristics, such as fingerprints,
face, iris, gait, and voice. Iris recognition is one of the most successful
methods because it exploits the rich texture of the human iris, which is unique
even for twins and does not degrade with age. Modern approaches to iris
recognition utilize deep learning to segment the valid portion of the iris from
the rest of the eye, so it can then be encoded, stored and compared. This paper
aims to improve the accuracy of iris semantic segmentation systems by
introducing a novel data augmentation technique. Our method can transform an
iris image with a certain dilation level into any desired dilation level, thus
augmenting the variability and number of training examples from a small
dataset. The proposed method is fast and does not require training. The results
indicate that our data augmentation method can improve segmentation accuracy up
to 15% for images with high pupil dilation, which creates a more reliable iris
recognition pipeline, even under extreme dilation.
