Department of Electronic Engineering, College of Engineering, National University of Ireland Galway, University Road, Galway, Ireland.
imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
Neural Netw. 2020 Jan;121:101-121. doi: 10.1016/j.neunet.2019.07.020. Epub 2019 Aug 1.
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.
提出了一种数据增强方法，并将其应用于生成大量离轴虹膜区域的数据集，并训练一个低复杂度的深度神经网络。尽管该网络复杂度低，但它在对具有挑战性的离轴眼片的虹膜区域分割中实现了高精度。有趣的是，该网络还在常规的、正面的虹膜区域分割中表现出了很高的性能，与复杂度明显更高的最新技术相比具有优势。由于其复杂度较低，该网络非常适合部署在嵌入式应用中，如增强现实和混合现实耳机。
