3,807 research outputs found
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Construction and evaluation of classifiers for forensic document analysis
In this study we illustrate a statistical approach to questioned document
examination. Specifically, we consider the construction of three classifiers
that predict the writer of a sample document based on categorical data. To
evaluate these classifiers, we use a data set with a large number of writers
and a small number of writing samples per writer. Since the resulting
classifiers were found to have near perfect accuracy using leave-one-out
cross-validation, we propose a novel Bayesian-based cross-validation method for
evaluating the classifiers.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS379 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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
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