Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Invariant representation and matching of space curves

Space curves are highly descriptive features for 3-D objects. Two invariant representations for space curves are discussed in this paper. One represents space curves by complex waveforms. The other represents space curves using the 3-D moment invariants of the data points on the curves. Space curve matching using invariant global features is discussed. An algorithm for matching partially occluded 3-D curves is also presented, in which rigidity constraints on pairwise curve segments are used to determine the globally consistent matching. An association graph can be constructed from the local matches. The maximal cliques of the graph will determine the visible part of the model curves in the scene. Experimental results using 3-D curves obtained from stereo matching and edges detected from the range data are also presented

Theory and applications of free-electron vortex states

Both classical and quantum waves can form vortices: with helical phase fronts and azimuthal current densities. These features determine the intrinsic orbital angular momentum carried by localized vortex states. In the past 25 years, optical vortex beams have become an inherent part of modern optics, with many remarkable achievements and applications. In the past decade, it has been realized and demonstrated that such vortex beams or wavepackets can also appear in free electron waves, in particular, in electron microscopy. Interest in free-electron vortex states quickly spread over different areas of physics: from basic aspects of quantum mechanics, via applications for fine probing of matter (including individual atoms), to high-energy particle collision and radiation processes. Here we provide a comprehensive review of theoretical and experimental studies in this emerging field of research. We describe the main properties of electron vortex states, experimental achievements and possible applications within transmission electron microscopy, as well as the possible role of vortex electrons in relativistic and high-energy processes. We aim to provide a balanced description including a pedagogical introduction, solid theoretical basis, and a wide range of practical details. Special attention is paid to translate theoretical insights into suggestions for future experiments, in electron microscopy and beyond, in any situation where free electrons occur.Comment: 87 pages, 34 figure

Pose Invariant 3D Face Authentication based on Gaussian Fields Approach

This thesis presents a novel illuminant invariant approach to recognize the identity of an individual from his 3D facial scan in any pose, by matching it with a set of frontal models stored in the gallery. In view of today’s security concerns, 3D face reconstruction and recognition has gained a significant position in computer vision research. The non intrusive nature of facial data acquisition makes face recognition one of the most popular approaches for biometrics-based identity recognition. Depth information of a 3D face can be used to solve the problems of illumination and pose variation associated with face recognition. The proposed method makes use of 3D geometric (point sets) face representations for recognizing faces. The use of 3D point sets to represent human faces in lieu of 2D texture makes this method robust to changes in illumination and pose. The method first automatically registers facial point-sets of the probe with the gallery models through a criterion based on Gaussian force fields. The registration method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. The new method overcomes the necessity of close initialization and converges in much less iterations as compared to the Iterative Closest Point algorithm. The use of an optimization method, the Fast Gauss Transform, allows a considerable reduction in the computational complexity of the registration algorithm. Recognition is then performed by using the robust similarity score generated by registering 3D point sets of faces. Our approach has been tested on a large database of 85 individuals with 521 scans at different poses, where the gallery and the probe images have been acquired at significantly different times. The results show the potential of our approach toward a fully pose and illumination invariant system. Our method can be successfully used as a potential biometric system in various applications such as mug shot matching, user verification and access control, and enhanced human computer interaction

Development of 1H-NMR Serum Profiling Methods for High-Throughput Metabolomics

El perfilat de sèrum per ressonància magnètica nuclear de protó (1H-RMN) està especialment indicat per a anàlisi a gran escala en estudis epidemiològics, nutricionals o farmacològics. L’espectroscòpia 1H-RMN requereix mínima manipulació de mostra i gràcies a la seva resposta quantitativa permet la comparació directa entre laboratoris. Un perfilat complet de sèrum per 1H-RMN requereix de tres mesures que es corresponen amb tres espècies moleculars diferents: lipoproteïnes, metabòlits de baix pes molecular i lípids. El perfilat de sèrum per 1H-RMN permet obtenir informació de grandària, nombre de partícules i contingut lipídic de les subfraccions lipoproteiques, així com l'abundància d'aminoàcids, productes de la glicòlisi, cossos cetònics, àcids grassos i fosfolípids, entre d'altres. No obstant això, la complexitat espectral afavoreix la inclusió d'errors en l'anàlisi manual de les dades, mentre que les múltiples interaccions moleculars en el sèrum comprometen la seva precisió quantitativa. És per tant necessari desenvolupar mètodes robustos de perfilat metabòlic per consolidar la 1H-RMN en la pràctica clínica. Per a això, aquesta tesi presenta diverses estratègies metodològiques i computacionals. En el primer treball, es van desenvolupar mètodes de regressió dels lípids del perfil lipídic clàssic, generalitzables a mostres de població sana i amb valors de lípids i lipoproteïnes anormals. Aquests lípids representen els principals indicadors de risc cardiovascular i els objectius terapèutics primaris. En el segon estudi caracteritzem els errors de quantificació en el perfilat 1H-RMN de metabòlits clínicament rellevants, que són deguts a la seva agregació a la proteïna sanguínia. També proposem un mètode que fomenta la competició per l'agregació i que ens permet obtenir quantificacions dels nostres metabòlits properes a les absolutes. Finalment, el tercer treball presenta LipSpin: una eina bioinformàtica de codi obert específicament dissenyada per al perfilat de lípids per 1H-RMN. A més, aquest estudi exposa alguns aspectes metodològics per millorar l'anàlisi de lípids per RMN.El perfilado de suero por resonancia magnética nuclear de protón (1H-RMN) está especialmente indicado para el análisis a gran escala en estudios epidemiológicos, nutricionales o farmacológicos. La espectroscopía 1H-RMN requiere mínima manipulación de muestra y gracias a su respuesta cuantitativa permite la comparación directa entre laboratorios. Un perfilado completo de suero por 1H-RMN requiere de tres mediciones que se corresponden con tres especies moleculares distintas: lipoproteínas, metabolitos de bajo peso molecular y lípidos. El perfilado de suero por 1H-RMN permite obtener información de tamaño, número de partículas y contenido lipídico de las subfracciones lipoproteicas, así como la abundancia de aminoácidos, productos de la glicólisis, cuerpos cetónicos, ácidos grasos y fosfolípidos, entre otros. Sin embargo, la complejidad espectral favorece la inclusión de errores en el análisis manual de los datos, mientras que las múltiples interacciones moleculares en el suero comprometen su precisión cuantitativa. Es por tanto necesario desarrollar métodos robustos de perfilado metabólico para consolidar la 1H-RMN en la práctica clínica. Para ello, esta tesis presenta varias estrategias metodológicas y computacionales. En el primer trabajo, se desarrollaron métodos de regresión de los lípidos del perfil lipídico clásico, generalizables a muestras de población sana y con valores de lípidos y lipoproteínas anormales. Estos lípidos representan los principales indicadores de riesgo cardiovascular y los objetivos terapéuticos primarios. En el segundo estudio caracterizamos los errores de cuantificación en el perfilado 1H-RMN de metabolitos clínicamente relevantes, que son debidos a su agregación a la proteína sanguínea. También proponemos un método que fomenta la competición por la agregación y que nos permite obtener cuantificaciones de nuestros metabolitos cercanas a las absolutas. Por último, el tercer trabajo presenta LipSpin: una herramienta bioinformática de código abierto específicamente diseñada para el perfilado de lípidos por 1H-RMN. Además, este estudio expone algunos aspectos metodológicos para mejorar el análisis de lípidos por RMN.1H-NMR serum profiling is especially suitable for high-throughput epidemiological studies and large-scale nutritional studies and drug monitoring. It requires minimal sample manipulation and its quantitative response allows inter-laboratory comparison. A comprehensive 1H-NMR serum profiling consists of three measurements encoding different molecular species: lipoproteins, low-molecular-weight metabolites and lipids. 1H-NMR serum profiling provides information of size, particle number and lipid content of lipoprotein subclasses, as well as abundance of amino acids, glycolysis-related metabolites, ketone bodies, fatty acids and phospholipids, among others. However, the spectral complexity promotes errors in manual data analysis and the multiple molecular interactions within the sample compromise reliable quantifications. Developing robust methods of metabolite serum profiling is therefore desirable to consolidate high-throughput 1H-NMR in the clinical practice. This thesis presents several methodological and computational strategies to that end. In the first study, we developed generalizable regression methods for lipids in routine clinical practice (known as “lipid panel”), to be applied in healthy population and in a wide spectrum of lipid and lipoprotein abnormalities. These standard lipids are still the main measurements of cardiovascular risk and therapy targets. In the second study, we characterised the quantitative errors introduced by protein binding in 1H-NMR profiling of clinically-relevant LMWM in native serum. Then, we proposed a competitive binding strategy to achieve quantifications closer to absolute concentrations, being fully compatible with high-throughput NMR. Finally, the third study presents LipSpin: an open source bioinformatics tool specifically designed for 1H-NMR profiling of serum lipids. Moreover, some methodological aspects to improve NMR-based serum lipid analysis are discussed

Improved methods for finger vein identification using composite median-wiener filter and hierarchical centroid features extraction

Finger vein identification is a potential new area in biometric systems. Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged because they reside underneath the skin of the finger and require a specific device to capture them. Research have been carried out in this field but there is still an unresolved issue related to low-quality data due to data capturing and processing. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. To address this issue, a new image enhancement and feature extraction methods were developed to improve finger vein identification. The image enhancement, Composite Median-Wiener (CMW) filter would improve image quality and preserve the edges of the finger vein image. Next, the feature extraction method, Hierarchical Centroid Feature Method (HCM) was fused with statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the one reported in the literature. As a conclusion, the results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification

Classification Algorithms based on Generalized Polynomial Chaos

Classification is one of the most important tasks in process system engineering. Since most of the classification algorithms are generally based on mathematical models, they inseparably involve the quantification and propagation of model uncertainty onto the variables used for classification. Such uncertainty may originate from either a lack of knowledge of the underlying process or from the intrinsic time varying phenomena such as unmeasured disturbances and noise. Often, model uncertainty has been modeled in a probabilistic way and Monte Carlo (MC) type sampling methods have been the method of choice for quantifying the effects of uncertainty. However, MC methods may be computationally prohibitive especially for nonlinear complex systems and systems involving many variables. Alternatively, stochastic spectral methods such as the generalized polynomial chaos (gPC) expansion have emerged as a promising technique that can be used for uncertainty quantification and propagation. Such methods can approximate the stochastic variables by a truncated gPC series where the coefficients of these series can be calculated by Galerkin projection with the mathematical models describing the process. Following these steps, the gPC expansion based methods can converge much faster to a solution than MC type sampling based methods. Using the gPC based uncertainty quantification and propagation method, this current project focuses on the following three problems: (i) fault detection and diagnosis (FDD) in the presence of stochastic faults entering the system; (ii) simultaneous optimal tuning of a FDD algorithm and a feedback controller to enhance the detectability of faults while mitigating the closed loop process variability; (iii) classification of apoptotic cells versus normal cells using morphological features identified from a stochastic image segmentation algorithm in combination with machine learning techniques. The algorithms developed in this work are shown to be highly efficient in terms of computational time, improved fault diagnosis and accurate classification of apoptotic versus normal cells

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Smart attendance monitoring system using computer vision.

Masters Degree. University of KwaZulu-Natal, Durban.Monitoring of student’s attendance remains the fundamental and vital part of any educational institution. The attendance of students to classes can have an impact on their academic performance. With the gradual increase in the number of students, it becomes a challenge for institutions to manage their attendance. The traditional attendance monitoring system requires considerable amount of time due to manual recording of names and circulation of the paper-based attendance sheet for students to sign their names. The paper-based attendance recording method and some existing automated systems such as mobile applications, Radio Frequency Identification (RFID), Bluetooth, and fingerprint attendance models are prone to fake results and time wasting. The limitations of the traditional attendance monitoring system stimulated the adoption of computer vision to stand in the gap. Student’s attendance can be monitored with biometric candidate’s systems such as iris recognition system and face recognition system. Among these, face recognition have a greater potential because of its non-intrusive nature. Although some automated attendance monitoring systems have been proposed, poor system modelling negatively affects the systems. In order to improve success of the automated systems, this research proposes the smart attendance monitoring system that uses facial recognition to monitor student’s attendance in a classroom. A time integrated model is provided to monitor student’s attendance throughout the lecture period by registering the attendance information at regular time intervals. Multi-camera system is also proposed to guarantee an accurate capturing of students. The proposed multi-camera based system is tested using a real-time database in an experimental class from the University of KwaZulu-Natal (UKZN). The results show that the proposed smart attendance monitoring System is reliable, with the average accuracy rate of 98%.Examiner's copy of thesis