Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

Viktor Varkarakis; Shabab Bazrafkan; Peter Corcoran

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arXiv · 1903.00389

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

Viktor Varkarakis·Shabab Bazrafkan·Peter Corcoran

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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.

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BibTeX RIS

Viktor Varkarakis, Shabab Bazrafkan, Peter Corcoran. 2019-03-01. Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets. https://arxiv.org/abs/1903.00389

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