Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Development of semi-automated steady state exogenous contrast cerebral blood volume mapping

Functional magnetic resonance imaging (fMRI) as it exists, in its many forms and vari- ants, has revolutionized the fields of neurology and psychology by revealing functional differences non-invasively. Although blood oxygenation level dependent (BOLD) fMRI is used interchangeably with fMRI, it measures one single difference in a phys- iological measurement using a set sequence. As such, there are other established changes in the brain that relate to blood movement and capacity that can also be measured using MRI. One measure, exogenous steady state cerebral blood volume, uses a bolus routine contrast agent administered intravenously alongside a pair of high resolution ‘structural-like’ MRI images to provide detailed information within small cortical and subcortical structures. In this thesis I design a semi-automated algorithm to generate maps of steady state exogenous cerebral blood volume magnetic resonance imaging datasets. To do this I developed an algorithm and tested it on existing MRI scanning protocols. A series of automated pre-processing steps are developed and tested, including automated scan flagging for artifacts and requisite vascular segmentation. Then, a methodology is developed to create cerebral blood volume (CBV) region of interest (ROI) masks that can then be applied on an existing database to test known CBV dysfunction in a group of patients at high risk for psychosis. Finally, we develop an experiment to see if template based cerebral blood alterations co-registered with class segmentation maps have any positive predictive value in determining disease state in a well characterized cohort of five age-matched groups in an Alzheimer’s disease neuroimaging study

Fragmentation and Wake Formation in Faint Meteors: Implications for the Structure and Ablation of Small Meteoroids

Meteors with peak magnitudes fainter than +2 are typically called faint meteors, resulting from the atmospheric entry and ablation of meteoroids less massive than 10-4 kg. The processes of luminous wake formation and fragmentation, which occur during ablation, are poorly understood for faint meteors, and are important constraints for models of meteoroid structure. The goal of this work is to improve understanding of these processes through analysis of high-resolution intensified video observations, and creation of a detailed meteoroid ablation model. In the first part of this work, thirty faint meteors observed with the Canadian Automated Meteor Observatory (CAMO) are analysed, revealing meteor trails with widths up to 100 m at heights above 110 km. These widths vary with height as the inverse of the atmospheric density, suggesting that formation of the wake is related to collisions between evaporated meteoric atoms and atmospheric molecules. Next, nine fragmenting faint meteors captured with CAMO are examined. Fragments from eight of the nine meteors are found to have transverse speeds up to 100 m s-1. These speeds are not explained by aerodynamic separation theory typically used for brighter meteors that fragment at lower heights. Instead, fragment separation by rotational breakup of the meteoroid or electrostatic repulsion are considered, giving meteoroid strength estimates up to 1 MPa. These strengths are typical of meteorite-producing meteoroids and are larger than expected for small meteoroids. Finally, a single-body ablation model, based on modelling collisions between the meteoroid, meteoric atoms, and atmospheric molecules, is devised to explain wake formation. Synthetic meteor trail widths and lengths, as well as light curves and deceleration profiles, are compared to observations of nine meteors from the first part of this thesis. The widths of simulated meteor wakes show good agreement with observations, but simulated wake lengths are too short. This suggests that collisional de-excitation of meteoric particles is a plausible process for wake formation, but also that meteoroid fragmentation likely increases the length of the meteor wake. Compared to observations, simulated light curves are longer, and simulated meteoroids experience less deceleration, suggesting that meteoroid fragmentation should be investigated in the next iteration of the model

Development of fluorescence lifetime measurement techniques for use in microfluidic channels

Fluorescence lifetime measurements are a powerful tool in biomedical research and advances in detection technology make them ideally suited for the study of biomolecular interactions. Time-resolved techniques, compared to more conventional methods, provide improved precision and contrast in the monitoring of complex biological processes. Fluorescence lifetimes are extracted by using time-correlated single-photon counting, which offers single photon sensitivity, high temporal resolution and excellent signal to noise ratio. Furthermore, combining this technique with microfluidics offers unprecedented advantages. For example, in analytical applications, apart from the high sensitivity required, the study of analytes often demands low sample consumption and short mixing times to allow for the monitoring of quick reactions. These parameters can nicely be achieved with the use of microfluidics. Hydrodynamic focusing within 3-inlet 1-outlet continuous flow microfluidic devices can be used as a molecular confinement mechanism to improve the detection efficiency as well as a means to enhance mixing within microchannels for the study of fast reaction kinetics. In this work, a powerful combination of confocal microscopy and microfluidics was used to perform fluorescence lifetime measurements on freely diffusing and freely flowing molecules. For this purpose, a home-built scanning confocal system was developed to ensure sufficient reduction in background levels, enabling the detection of fluorescence signal that arises from single molecules. Fluorescence lifetime imaging along with a maximum likelihood estimator adapted from single molecule studies was performed to visualise hydrodynamic focusing and characterise mixing within microfluidic devices. Time-resolved methods were also employed to detect single molecules freely flowing within microchannels. A novel fluorescence lifetime approach was developed to perform Förster resonance energy transfer measurements on freely diffusing molecules and subsequently applied for the study of streptavidin-biotin binding and protein conformational changes upon unfolding

Visualisation methods for polarimetric imaging

Polarimetric imaging is a technique for measuring the spatial correlations of the aspects of the polarisation of light. Since human vision is essentially unable to detect polarisation, the data obtained from this imaging technique must be converted into the channels of the human visual system in order to visually process the spatial correlations in the data. The technique for converting non-visual data into a visual representation is known as data visualisation. While the techniques for visualising other types of data is well studied, techniques specific for polarimetric imaging are understudied. This research aims to survey the current state of polarimetric imaging visualisation, to analyse the current methods using metrics from visualisation research, to improve on the existing techniques, to test the effectiveness of different methods in terms of user performance, and to develop novel colourmapping methods

Optimising Geopolymer Formation

Geopolymers are versatile materials, often made with ash from coal Power Stations. Applications include low green-house-gas emission cement, fireproof barriers and many more. This thesis furthered the understanding of geopolymer formulation by: • Demonstrating novel methods for mixture design and determining the degree of reaction during and after curing. • Analysing the role of formulation on cost and green-house-gas emission. • Developing a new material that can be used for structural neutron shielding

Contributions of Continuous Max-Flow Theory to Medical Image Processing

Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to analytically solvable optimization problems or guaranteed only local optimizability to increasingly complex and non-convex functionals, current methods based now rely on describing an optimization problem in a series of general yet simple functionals with a global, but non-analytic, solution algorithms. This has been increasingly spurred on by the availability of these general-purpose algorithms in an open-source context. Thus, graph-cuts and max-flow have changed every aspect of medical image processing from reconstruction to enhancement to segmentation and registration. To wax philosophical, continuous max-flow theory in particular has the potential to bring a high degree of mathematical elegance to the field, bridging the conceptual gap between the discrete and continuous domains in which we describe different imaging problems, properties and processes. In Chapter 1, we use the notion of infinitely dense and infinitely densely connected graphs to transfer between the discrete and continuous domains, which has a certain sense of mathematical pedantry to it, but the resulting variational energy equations have a sense of elegance and charm. As any application of the principle of duality, the variational equations have an enigmatic side that can only be decoded with time and patience. The goal of this thesis is to show the contributions of max-flow theory through image enhancement and segmentation, increasing incorporation of topological considerations and increasing the role played by user knowledge and interactivity. These methods will be rigorously grounded in calculus of variations, guaranteeing fuzzy optimality and providing multiple solution approaches to addressing each individual problem

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã