Name
Title | ||
|---|---|---|
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets. |
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 favourably 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neunet.2019.07.020 | Neural Networks |
Keywords | Field | DocType |
Deep neural networks,Data augmentation,Off-axis,Iris segmentation,AR/VR | Software deployment,Pattern recognition,Wearable computer,Computer science,Segmentation,Embedded applications,Artificial intelligence,Mixed reality,Artificial neural network | Journal |
Volume | Issue | ISSN |
121 | 1 | 0893-6080 |
Citations | PageRank | References |
4 | 0.42 | 38 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viktor Varkarakis | 1 | 4 | 1.10 |
| S. Bazrafkan | 2 | 58 | 5.44 |
| P. M. Corcoran | 3 | 414 | 82.56 |