# Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation

arXiv: https://arxiv.org/abs/2212.12733 · HTML: https://mlsanity.com/paper/2212.12733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [CycleGAN, a Master of Steganography](https://mlsanity.com/paper/1712.02950/) (2017)
- [CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation](https://mlsanity.com/paper/1901.01578/) (2019)
- [DeepIris: Iris Recognition Using A Deep Learning Approach](https://mlsanity.com/paper/1907.09380/) (2019)
- [Open Source Iris Recognition Hardware and Software with Presentation Attack Detection](https://mlsanity.com/paper/2008.08220/) (2020)
- [Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects](https://mlsanity.com/paper/2106.15828/) (2021)
- [Synthetic Periocular Iris PAI from a Small Set of Near-Infrared-Images](https://mlsanity.com/paper/2107.12014/) (2021)

## Abstract (arXiv)

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.