SuperIdentity: fusion of identity across real and cyber domains

Under both benign and malign circumstances, people now manage a spectrum of identities across both real-world and cyber domains. Our belief, however, is that all these instances ultimately track back for an individual to reflect a single ‘SuperIdentity’. This paper outlines the assumptions underpinning the SuperIdentity Project, describing the innovative use of data fusion to incorporate novel real-world and cyber cues into a rich framework appropriate for modern identity. The proposed combinatorial model will support a robust identification or authentication decision, with confidence indexed both by the level of trust in data provenance, and the diagnosticity of the identity factors being used. Additionally, the exploration of correlations between factors may underpin the more intelligent use of identity information so that known information may be used to predict previously hidden information. With modern living supporting the ‘distribution of identity’ across real and cyber domains, and with criminal elements operating in increasingly sophisticated ways in the hinterland between the two, this approach is suggested as a way forwards, and is discussed in terms of its impact on privacy, security, and the detection of threa

e-Authentication for online assessment: A mixed-method study

Authenticating the students’ identity and authenticity of their work is increasingly important to reduce academic malpractices and for quality assurance purposes in Education. There is a growing body of research about technological innovations to combat cheating and plagiarism. However, the literature is very limited on the impact of e-authentication systems across distinctive end-users because it is not a widespread practice at the moment. A considerable gap is to understand whether the use of e-authentication systems would increase trust on e-assessment, and to extend, whether students’ acceptance would vary across gender, age and previous experiences. This study aims to shed light on this area by examining the attitudes and experiences of 328 students who used an authentication system known as adaptive trust-based e-assessment system for learning (TeSLA). Evidence from mixed-method analysis suggests a broadly positive acceptance of these e-authentication technologies by distance education students. However, significant differences in the students’ responses indicated, for instance, that men were less concerned about providing personal data than women; middle-aged participants were more aware of the nuances of cheating and plagiarism;while younger students were more likely to reject e-authentication, considerably due to data privacy and security and students with disabilities due to concerns about their special needs

3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps

In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation