Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation
This work addresses the challenge of limited training data variability in iris recognition systems, particularly under extreme pupil dilation conditions, though it is incremental as it builds on existing segmentation methods.
The paper tackled the problem of improving iris semantic segmentation accuracy by introducing a novel data augmentation technique that transforms iris images to any desired pupil dilation level, resulting in up to a 15% improvement in segmentation accuracy for images with high pupil dilation.
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.