463 research outputs found
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
E-INVIGILATION OF E-ASSESSMENTS
E-learning and particularly distance-based learning is becoming an increasingly important mechanism for education. A leading Virtual Learning Environment (VLE) reports a user base of 70 million students and 1.2 million teachers across 7.5 million courses. Whilst e-learning has introduced flexibility and remote/distance-based learning, there are still aspects of course delivery that rely upon traditional approaches. The most significant of these is examinations. The lack of being able to provide invigilation in a remote-mode has restricted the types of assessments, with exams or in-class test assessments proving difficult to validate. Students are still required to attend physical testing centres in order to ensure strict examination conditions are applied. Whilst research has begun to propose solutions in this respect, they fundamentally fail to provide the integrity required. This thesis seeks to research and develop an e-invigilator that will provide continuous and transparent invigilation of the individual undertaking an electronic based exam or test. The analysis of the e-invigilation solutions has shown that the suggested approaches to minimise cheating behaviours during the online test have varied. They have suffered from a wide range of weaknesses and lacked an implementation achieving continuous and transparent authentication with appropriate security restrictions. To this end, the most transparent biometric approaches are identified to be incorporated in an appropriate solution whilst maintaining security beyond the point-of-entry.
Given the existing issues of intrusiveness and point-of-entry user authentication, a complete architecture has been developed based upon maintaining student convenience but providing effective identity verification throughout the test, rather than merely at the beginning. It also provides continuous system-level monitoring to prevent cheating, as well as a variety of management-level functionalities for creating and managing assessments including a prioritised and usable interface in order to enable the academics to quickly verify and check cases of possible cheating. The research includes a detailed discussion of the architecture requirements, components, and complete design to be the core of the system which captures, processes, and monitors students in a completely controlled e-test environment.
In order to highlight the ease of use and lightweight nature of the system, a prototype was developed. Employing student face recognition as the most transparent multimodal (2D and 3D modes) biometrics, and novel security features through eye tracking, head movements, speech recognition, and multiple faces detection in order to enable a robust and flexible e-invigilation approach. Therefore, an experiment (Experiment 1) has been conducted utilising the developed prototype involving 51 participants. In this experiment, the focus has been mainly upon the usability of the system under normal use. The FRR of those 51 legitimate participants was 0 for every participant in the 2D mode; however, it was 0 for 45 of them and less than 0.096 for the rest 6 in the 3D mode. Consequently, for all the 51 participants of this experiment, on average, the FRR was 0 in 2D facial recognition mode, however, in 3D facial recognition mode, it was 0.048. Furthermore, in order to evaluate the robustness of the approach against targeted misuse 3 participants were tasked with a series of scenarios that map to typical misuse (Experiment 2). The FAR was 0.038 in the 2D mode and 0 in the 3D mode. The results of both experiments support the feasibility, security, and applicability of the suggested system.
Finally, a series of scenario-based evaluations, involving the three separate stakeholders namely: Experts, Academics (qualitative-based surveys) and Students (a quantitative-based and qualitative-based survey) have also been utilised to provide a comprehensive evaluation into the effectiveness of the proposed approach. The vast majority of the interview/feedback outcomes can be considered as positive, constructive and valuable. The respondents agree with the idea of continuous and transparent authentication in e-assessments as it is vital for ensuring solid and convenient security beyond the point-of-entry. The outcomes have also supported the feasibility and practicality of the approach, as well as the efficiency of the system management via well-designed and smart interfaces.The Higher Committee for Education Development in Iraq (HCED
A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms
In this paper a review is presented of the research on eye gaze estimation
techniques and applications, that has progressed in diverse ways over the past
two decades. Several generic eye gaze use-cases are identified: desktop, TV,
head-mounted, automotive and handheld devices. Analysis of the literature leads
to the identification of several platform specific factors that influence gaze
tracking accuracy. A key outcome from this review is the realization of a need
to develop standardized methodologies for performance evaluation of gaze
tracking systems and achieve consistency in their specification and comparative
evaluation. To address this need, the concept of a methodological framework for
practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July
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