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

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

Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets

StyleGAN is a state-of-art generative adversarial network architecture that generates random 2D high-quality synthetic facial data samples. In this paper, we recap the StyleGAN architecture and training methodology and present our experiences of retraining it on a number of alternative public datasets. Practical issues and challenges arising from the retraining process are discussed. Tests and validation results are presented and a comparative analysis of several different re-trained StyleGAN weightings is provided 1. The role of this tool in building large, scalable datasets of synthetic facial data is also discussed

Contributions to data augmentation techniques and synthetic data for training deep neural networks

In the recent years deep learning has become more and more popular and it is applied in a variety of fields, yielding outstanding results in different machine learning applications. Deep learning based solutions thrive when a large amount of data is available for a specific problem but data availability and preparation are the biggest bottlenecks in the deep learning pipelines. With the fast-changing technology environment, new unique problems arise daily. In order to realise solutions in many of these specific problem domains there is a growing need to build custom datasets that are tailored for a particular use case with matching ground truth data. Acquiring such datasets at the scale required for training with today’s AI systems and subsequently annotating them with an accurate ground truth is challenging. Furthermore, with the recent introduction of GDPR and associated complications introduced, industry now faces additional challenges in the collection of training data that is linked to individual persons. This dissertation focuses on ways to overcome the unavailability of real data and avoid the challenges that come with a data acquisition process. More specifically data augmentation techniques are proposed to overcome the unavailability of real data, improve performance and allow the use of low-complexity models, suitable for implementation in edge devices. Furthermore, the idea of using AI tools to build large synthetic datasets is considered as an alternative to real data samples. The first steps in order to build and incorporate synthetic datasets effectively into the deep learning training pipelines include: building AI tools, that will generate a large amount of new data and/or augment these data samples and also create methodologies and techniques to validate that the generate data behave like real ones and also measure whether their use is effective when incorporated in the training pipelines, with this dissertation contributing to both of these steps

Deep learning for consumer devices and services 2-AI gets embedded at the edge

The recent explosive growth of deep learning is enabling a new generation of intelligent consumer devices. Specialized deep learning inference now provides data analysis capabilities that once required an active cloud connection, while reducing latency and enhancing data privacy. This paper addresses current progress in Edge artificial intelligence (AI) technology in several consumer contexts including privacy, biometrics, eye gaze, driver monitoring systems, and more. New developments and challenges in edge hardware and emerging opportunities are identified. Our previous article, "Deep learning for consumer devices and services," introduced many of the basics of deep learning and AI. In this paper, we explore the current paradigm shift of AI from the data center into CE devices-"Edge-AI."This work was supported in part by the SFI Strategic Partnership Program by Science Foundation Ireland and FotoNation, Ltd., under Project 13/SPP/I2868 on Next Generation Imaging for Smartphone and Embedded Platforms, and in part by an Irish Research Council Employment-Based Programme Award under Project EBPPG/2016/280.peer-reviewe

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

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. (C) 2019 Elsevier Ltd. All rights reserved.This research is funded under the SFI Strategic Partnership Program by Science Foundation Ireland (SFI) and FotoNation Ltd. Project ID: 13/SPP/I2868 on Next Generation Imaging for Smart- phone and Embedded Platforms. Portions of the research in this paper use the CASIA-IrisV4 collected by the Chinese Academy of Sciences’ Institute of Automation (CASIA).peer-reviewed2021-08-0