Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets. (January 2020)

• Record Type: Journal Article
• Authors: Varkarakis, Viktor; Bazrafkan, Shabab; Corcoran, Peter
• Is Part Of: Neural networks. Volume 121(2020)
• Page Start: 101
• Page End: 121
• Publication Date: 2020-01
• Subjects: Deep neural networks -- Data augmentation -- Off-axis -- Iris segmentation -- AR/VR; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.07.020
• Languages: English
• ISSNs: 0893-6080

Abstract

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.