My Journey into Glass: Talking about Google Glass with stakeholders in the Glass Explorer Program

Alexander, Hayes

doi:10.1109/mce.2015.2484819

Cited by 3 publications

(2 citation statements)

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“…In fact researchers have been working with Augmented Displays for more than 20 years [17]- [23]. The most recent mass market experiment with a wearable, augmented/mediated-reality display, that could be worn on a day-to-day basis, was Google Glass [22], [24], [25]. Glass, as it became known, was considered to be a game changing technology for a few years across a wide range of industry sectors [26]- [29].…”

Section: Introductionmentioning

confidence: 99%

“…A key challenge with AR/VR headsets is that, lacking a physical keyboard they do not provide an intuitive mean of user authentication. The weak authentication available in Glass [25], [31], [32] subsequently led to various attempts to refine and improve on the basic authentication of the headset [33]- [35]. Ultimately, the device authentication was simply not adequate and led, in part, to its withdrawal from the market.…”

Section: Introductionmentioning

confidence: 99%

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Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

2020

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A data augmentation methodology is presented and applied to generate a large dataset of offaxis 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. Liu,in [72] proposed two CNN approaches to segment noisy iris images acquired under unconstrained conditions. In the first approach called hierarchical convolutional neural networks (HCNNs), three patches taken from different scales of the same image are used as input. The HCNN consists of three similar blocks, a combination of convolutional and pooling layers that are merged together into a fully connected layer. In the second approach, 31 convolutional layers and 6 pooling layers are used to compose the multi-scale fully convolutional network (MFCNs). Both models are end-to-end, with no requirement for pre-or post-processing of the image. Arsalan [73], introduced a two-stage iris segmentation method. The first stage includes a pre-processing of the image and the use of a modified Hough Transform to identify the region of interest (ROI). In the second stage, a mask of [21 × 21] pixels, based on the ROI defined in the previous stage, is fed to a pre-trained VGG-face model which classifies the pixels as iris or non-iris. In a follow up work which is focused on segmenting low quality iris images, Arsalan in [74], proposed a densely connected fully convolutional network (IrisDenseNet), consisting of two main components: a densely connected encoder and a SegNet decoder. In a similar work, Bazrafkan in [43], presented a network design focused on segmenting iris of inferior quality. Four different end-to-end fully convolutional networks are merged into a single model using a method known as Semi Parallel Deep Neural Networks (SPDNN). In this way, the final model benefits from each of the four distinct network designs. Finally, since the existence of a large labelled dataset is a prerequisite in order to implement a convolutional neural network approach, Jalilian in [75] to overcome this obstacle, introduced a domain adaption method so that a CNN for iris segmentation could be trained with a limited data. ContributionsThe focus of this work is to improve the segmentation of off-axis iris images originating from the unconstrained conditions of a user-facing camera on wearable AR/VR device.The model proposed is an end to end deep neural network which accepts an off-axis eye-region image and generates the corresponding binary segmentation map for the iris region as output. Performance evaluation of the proposed model shows advantages over recent iris segmentation tec...

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