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

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

The Use of EEG Signals For Biometric Person Recognition

This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases

EEG-based biometrics: Effects of template ageing

This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vector Machines and k-Nearest Neighbours with different parametrisations. Results show that performance tends to be worse for crosssession identification compared to single session identification. This finding suggests that temporal permanence of EEG signals is limited and thus more sophisticated methods are needed in order to characterise EEG signals for the task of subject identificatio

Assessing the effects of repeated handling on physiology and condition of semi-precocial nestlings

Repeated exposure to elevated levels of glucocorticoids during development can have long-term detrimental effects on survival and fitness, potentially associated with increased telomere attrition. Nestling birds are regularly handled for ecological research, yet few authors have considered the potential for handling-induced stress to influence hormonally-mediated phenotypic development or bias interpretations of subsequent focal measurements. We experimentally manipulated the handling experience of the semi-precocial nestlings of European Storm Petrel Hydrobates pelagicus to simulate handling in a typical field study and examined cumulative effects on physiology and condition in late postnatal development. Neither baseline corticosterone (the primary glucocorticoid in birds), telomere length nor body condition varied with the number of handling episodes. The absence of a response could be explained if Storm Petrels did not perceive handling to be stressful or if there is dissociation of the hypothalamic-pituitary-adrenal axis from stressful stimuli in early life. Eliciting a response to a stressor may be maladaptive for cavity-dwelling young that are unable to escape or defend themselves. Furthermore, avoiding elevated overall glucocorticoid exposure may be particularly important in a long-lived species, in which accelerated early-life telomere erosion could impact negatively upon longevity. We propose that the level of colony-wide disturbance induced by investigator handling of young could be important in underlining species-specific responses. Storm Petrel nestlings appear unresponsive to investigator handling within the limits of handling in a typical field study and handling at this level should not bias physiological and morphological measurements

Potential mechanism of action of J5 vaccine in protection against severe bovine coliform mastitis.

Coliform mastitis is one of the most difficult diseases to treat in the modern dairy industry. Curative therapy with antibiotics remains only moderately effective and depends on the stage at which the disease is treated, The most successful strategies for combating coliform. mastitis appear to be prevention by hygienic management or prophylactic immunization. The severity of clinical symptoms of coliform mastitis has been shown to be reduced by immunization with the Escherichia coli J5 vaccine. However, although the J5 vaccine has been licensed in the United States for about 10 years, the immunological basis of its mechanism of action is still unknown. Until now, protection by J5 vaccination has often been explained by a straight forward mechanism of enhanced antibody production resulting in increased opsonization of coliform bacteria and lipopolysaccharides (LPS). The possibility that J5 vaccination could decrease risk factors for coliform mastitis such as impaired blood polymorphonuclcar neutrophil leukocyte (PMN) diapedesis has never been investigated. This review provides arguments to support the hypothesis that J5 vaccination may reduce the severity of coliform mastitis by inducing a condition of mammary gland hyper-responsiveness, characterized by a T helper 1 (Th1) response and mediated by memory cells inside the mammary gland, finally resulting in enhanced PMN diapedesis upon an intramammary infection

Age Differences in Intra-Individual Variability in Simple and Choice Reaction Time: Systematic Review and Meta-Analysis

Intra-individual variability in reaction time (RT IIV) is considered to be an index of central nervous system functioning. Such variability is elevated in neurodegenerative diseases or following traumatic brain injury. It has also been suggested to increase with age in healthy ageing.To investigate and quantify age differences in RT IIV in healthy ageing; to examine the effect of different tasks and procedures; to compare raw and mean-adjusted measures of RT IIV.Four electronic databases: PsycINFO, Medline, Web of Science and EMBASE, and hand searching of reference lists of relevant studies.English language journal articles, books or book chapters, containing quantitative empirical data on simple and/or choice RT IIV. Samples had to include younger (under 60 years) and older (60 years and above) human adults.Studies were evaluated in terms of sample representativeness and data treatment. Relevant data were extracted, using a specially-designed form, from the published report or obtained directly from the study authors. Age-group differences in raw and RT-mean-adjusted measures of simple and choice RT IIV were quantified using random effects meta-analyses.Older adults (60+ years) had greater RT IIV than younger (20-39) and middle-aged (40-59) adults. Age effects were larger in choice RT tasks than in simple RT tasks. For all measures of RT IIV, effect sizes were larger for the comparisons between older and younger adults than between older and middle-aged adults, indicating that the age-related increases in RT IIV are not limited to old age. Effect sizes were also larger for raw than for RT-mean-adjusted RT IIV measures.RT IIV is greater among older adults. Some (but not all) of the age-related increases in RT IIV are accounted for by the increased RT means