Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
Computer Vision and Pattern Recognition
2019-03-04 v1
Abstract
A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets.
Cite
@article{arxiv.1903.00389,
title = {Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets},
author = {Viktor Varkarakis and Shabab Bazrafkan and Peter Corcoran},
journal= {arXiv preprint arXiv:1903.00389},
year = {2019}
}