A Highly Efficient Biometrics Approach for Unconstrained Iris Segmentation and Recognition

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineats the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design

Assessing the match performance of non-ideal operational facial images using 3D image data.

Biometric attributes are unique characteristics specific to an individual, which can be used in automated identification schemes. There have been considerable advancements in the field of face recognition recently, but challenges still exist. One of these challenges is pose-variation, specifically, roll, pitch, and yaw variations away from a frontal image. The goal of this problem report is to assess the improvement of facial recognition performance obtainable by commercial pose-correction software. This was done using pose-corrected images obtained in two ways: 1) non-frontal images generated and corrected using 3D facial scans (pseudo-pose-correction) and 2) the same non-frontal images corrected using FaceVACs DBScan. Two matchers were used to evaluate matching performance namely Cognitec FaceVACs and MegaMatcher 5.0 SDK. A set of matching experiments were conducted using frontal, non-frontal and pose-corrected images to assess the improvement in matching performance, including: 1. Frontal (probe) to Frontal (gallery) images, to generate the baseline 2. Non-ideal pose-varying (probe) to frontal (gallery) 3. Pseudo-pose-corrected (probe) to frontal (gallery) 4. Auto-pose-corrected (probe) to frontal (gallery). Cumulative match characteristics curves (CMC) are used to evaluate the performance of the match scores generated. These matching results have shown better performance in case of pseudo-pose-corrected images compared to the non-frontal images, where the rank accuracy is 100% for the angles which were not detected by the matchers in the non-frontal case. Of the two commercial matchers, Cognitec, which is software optimized for non-frontal models, has shown a better performance in detection of face with angular rotations. MegaMatcher, which is not a pose-correction matcher, was unable to detect greater angles of rotation which are 50° and 60° in pitch, greater than 40° for yaw and for coupled pitch/yaw it was unable to detect 4 out of 8 combinations. The requirements of the facial recognition application will influence the decision to implement pose correction tools

Face recognition committee machine: methodology, experiments, and a system application.

Tang Ho-Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 85-92).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Face Recognition --- p.2Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- Organization of this Thesis --- p.6Chapter 2 --- Literature Review --- p.8Chapter 2.1 --- Committee Machine --- p.8Chapter 2.1.1 --- Static Structure --- p.9Chapter 2.1.2 --- Dynamic Structure --- p.10Chapter 2.2 --- Face Recognition Algorithms Overview --- p.11Chapter 2.2.1 --- Eigenface --- p.12Chapter 2.2.2 --- Fisherface --- p.17Chapter 2.2.3 --- Elastic Graph Matching --- p.19Chapter 2.2.4 --- Support Vector Machines --- p.23Chapter 2.2.5 --- Neural Networks --- p.25Chapter 2.3 --- Commercial System and Applications --- p.27Chapter 2.3.1 --- FaceIT --- p.28Chapter 2.3.2 --- ZN-Face --- p.28Chapter 2.3.3 --- TrueFace --- p.29Chapter 2.3.4 --- Viisage --- p.30Chapter 3 --- Static Structure --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Architecture --- p.32Chapter 3.3 --- Result and Confidence --- p.33Chapter 3.3.1 --- "Eigenface, Fisherface, EGM" --- p.34Chapter 3.3.2 --- SVM --- p.35Chapter 3.3.3 --- Neural Networks --- p.36Chapter 3.4 --- Weight --- p.37Chapter 3.5 --- Voting Machine --- p.38Chapter 4 --- Dynamic Structure --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Architecture --- p.41Chapter 4.3 --- Gating Network --- p.42Chapter 4.4 --- Feedback Mechanism --- p.44Chapter 5 --- Face Recognition System --- p.46Chapter 5.1 --- Introduction --- p.46Chapter 5.2 --- System Architecture --- p.47Chapter 5.2.1 --- Face Detection Module --- p.48Chapter 5.2.2 --- Face Recognition Module --- p.49Chapter 5.3 --- Face Recognition Process --- p.50Chapter 5.3.1 --- Enrollment --- p.51Chapter 5.3.2 --- Recognition --- p.52Chapter 5.4 --- Distributed System --- p.54Chapter 5.4.1 --- Problems --- p.55Chapter 5.4.2 --- Distributed Architecture --- p.56Chapter 5.5 --- Conclusion --- p.59Chapter 6 --- Experimental Results --- p.60Chapter 6.1 --- Introduction --- p.60Chapter 6.2 --- Database --- p.61Chapter 6.2.1 --- ORL Face Database --- p.61Chapter 6.2.2 --- Yale Face Database --- p.62Chapter 6.2.3 --- AR Face Database --- p.62Chapter 6.2.4 --- HRL Face Database --- p.63Chapter 6.3 --- Experimental Details --- p.64Chapter 6.3.1 --- Pre-processing --- p.64Chapter 6.3.2 --- Cross Validation --- p.67Chapter 6.3.3 --- System details --- p.68Chapter 6.4 --- Result --- p.69Chapter 6.4.1 --- ORL Result --- p.69Chapter 6.4.2 --- Yale Result --- p.72Chapter 6.4.3 --- AR Result --- p.73Chapter 6.4.4 --- HRL Result --- p.75Chapter 6.4.5 --- Average Running Time --- p.76Chapter 6.5 --- Discussion --- p.77Chapter 6.5.1 --- Advantages --- p.78Chapter 6.5.2 --- Disadvantages --- p.79Chapter 6.6 --- Conclusion --- p.80Chapter 7 --- Conclusion --- p.82Bibliography --- p.9

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

ATHENA Research Book

The ATHENA European University is an alliance of nine Higher Education Institutions with the mission of fostering excellence in research and innovation by facilitating international cooperation. The ATHENA acronym stands for Advanced Technologies in Higher Education Alliance. The partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal, and Slovenia: the University of Orléans, the University of Siegen, the Hellenic Mediterranean University, the Niccolò Cusano University, the Vilnius Gediminas Technical University, the Polytechnic Institute of Porto, and the University of Maribor. In 2022 institutions from Poland and Spain joined the alliance: the Maria Curie-Skłodowska University and the University of Vigo. This research book presents a selection of the ATHENA university partners' research activities. It incorporates peer-reviewed original articles, reprints and student contributions. The ATHENA Research Book provides a platform that promotes joint and interdisciplinary research projects of both advanced and early-career researchers

Person Recognition in Low-Quality Imagery.

PhD thesesPerson recognition aims to recognise and track the same individuals over space and time with subtle identity class information in automatically detected person images captured by unconstrained camera views. There are multi-source visual biometrical cues for person identity recognition. Specifically, compared to other widely-used cues that tend to easily change over time and space, the facial appearance is considered as a more reliable non-intrusive visual cue. Person recognition, especially the person face recognition, enables a wide range of practical applications, ranging from law enforcement and information security to business, entertainment and e-commerce. However, person recognition under realistic application scenarios remains significantly challenging, mainly due to the usual low resolutions (LR) of the images captured by low-quality cameras with unconstrained distances between cameras and people. Compared to the high-resolution (HR) images, the LR person images contain much less fine-grained discriminative details for robust identity recognition. To tackle the challenge of person recognition on low-resolution imagery data, one effective approach is to utilise the super resolution (SR) methods to recover or enhance the image details that are beneficial for identity recognition. However, this thesis reveals that conventional SR models suffer from significant performance drop when applied to low-quality LR person images, especially the natively captured surveillance facial images. Moreover, as the SR and identity recognition models advance independently, direct super resolution is less compatible with identity recognition, and hence has minor benefit or even negative effect for low-resolution person recognition. To tackle the above problems, this thesis explores person recognition methods with improved generalisation ability to realistic low-quality person images, by adopting dedicated superresolution algorithms. More specifically, this thesis addresses the issues for person face recognition and body recognition in low-resolution images as follows: Chapter 3 Whilst recent person face recognition techniques have made significant progress on recognising constrained high-resolution web images, the same cannot be said on natively unconstrained low-resolution images at large scales. This chapter examines systematically this under-studied person face recognition problem, and introduce a novel Complement Super-Resolution and Identity (CSRI) joint deep learning method with a unified end-to-end network architecture. The proposed learning mechanism is dedicated to overcome the inherent challenge of genuine low-resolution, concerning with the absence of HR facial images coupled with native LR faces, typically required for optimising image super-resolution models. This is realised by transferring the super-resolving knowledge from good-quality HR web images to the genuine LR facial data subject to the face identity label constraints of native LR faces in every mini-batch training. This chapter further constructs a new large-scale dataset TinyFace of native unconstrained low-resolution face images from selected public datasets. The extensive experiments show that there is a significant gap between the reported person face recognition performances on popular benchmarks and the results on TinyFace, and the advantages of the proposed CSRI over a variety of state-of-the-art face recognition and super-resolution deep models on solving this largely ignored person face recognition scenario. However, the lack of supervision in pixel space leads to the low-fidelity super-resolved images. which may hinder the further downstream facial analysis applications. Chapter 4 Although with a more advanced joint-learning scheme for person face recognition by super resolution (introduced in Chapter 3), by no-means one can claim that the proposed method solves the real-world low-resolution face recognition problem, which remains a significantly challenging task. In terms of human understanding, when people are faced with a challenging face identity recognition task, they often make decisions by selecting discriminative facial features. If a recognition model can be optimised with results that can be explained in a human-understandable way, such an interpretable model may have the potential to shed light on discriminative facial features selection for better identity recognition. To achieve this, recognising faces from high-fidelity super-resolved outputs could be a viable approach. However, existing facial super-resolution methods focus mostly on improving “artificially down-sampled” low-resolution (LR) imagery. Such SR models, although strong at handling artificial LR images, often suffer from significant performance drop on genuine LR test data. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data as well as cycle consistency loss formulation. However, this renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. Importantly, this makes the end-to-end model training ineffective due to the difficulty of back-propagating gradients through two concatenated CNNs. To solve this problem, in this chapter, a method that joins the advantages of conventional SR and UDA models is formulated. Specifically, the optimisations for characteristics consistifying and image super-resolving are separated and controlled by introducing Characteristic Regularisation (CR) between them. This task split makes the model training more effective and computationally tractable, and enables the high-fidelity super resolution process on genuine low-resolution faces. Chapter 5 Although the facial appearance is a more reliable visual cue for person recognition, it is often challenging or even impossible to detect the facial region in images captured by unconstrained low-quality cameras, where the faces can be of extreme poses, blur, distortion, or even invisible in the human back-view images. In such cases, the person body recognition is an important aspect for identity recognition and tracking. However, person images captured by unconstrained surveillance cameras often have low resolutions (LR). This causes the resolution mismatch problem when matched against the high-resolution (HR) gallery images, negatively affecting the performance of person body recognition. An effective approach is to leverage image super-resolution (SR) along with body recognition in a joint learning manner. However, this scheme is limited due to dramatically more difficult gradients backpropagation during training. This chapter introduces a novel model training regularisation method, called Inter-Task Association Critic (INTACT), to address this fundamental problem. Specifically, INTACT discovers the underlying association knowledge between image SR and person body recognition, and leverages it as an extra learning constraint for enhancing the compatibility of SR model with person body recognition in HR image space. This is realised by parameterising the association constraint, which can be automatically learned from the training data. Extensive experiments validate the superiority of INTACT over the state-of-the-art approaches on the cross-resolution person body recognition task using five standard datasets. Chapter 6 draws conclusions and suggests future works on open questions arising from the studies of this thesis

Development of a secure multi-factor authentication algorithm for mobile money applications

A Thesis Submitted in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyWith the evolution of industry 4.0, financial technologies have become paramount and mobile money as one of the financial technologies has immensely contributed to improving financial inclusion among the unbanked population. Several mobile money schemes were developed but, they suffered severe authentication security challenges since they implemented two-factor authentication. This study focused on developing a secure multi-factor authentication (MFA) algorithm for mobile money applications. It uses personal identification numbers, one-time passwords, biometric fingerprints, and quick response codes to authenticate and authorize mobile money subscribers. Secure hash algorithm-256, Rivest-Shamir-Adleman encryption, and Fernet encryption were used to secure the authentication factors, confidential financial information and data before transmission to the remote databases. A literature review, survey, evolutionary prototyping model, and heuristic evaluation and usability testing methods were used to identify authentication issues, develop prototypes of native genuine mobile money (G-MoMo) applications, and identify usability issues with the interface designs and ascertain their usability, respectively. The results of the review grouped the threat models into attacks against privacy, authentication, confidentiality, integrity, and availability. The survey identified authentication attacks, identity theft, phishing attacks, and PIN sharing as the key mobile money systems’ security issues. The researcher designed a secure MFA algorithm for mobile money applications and developed three native G-MoMo applications to implement the designed algorithm to prove the feasibility of the algorithm and that it provided robust security. The algorithm was resilient to non-repudiation, ensured strong authentication security, data confidentiality, integrity, privacy, and user anonymity, was highly effective against several attacks but had high communication overhead and computational costs. Nevertheless, the heuristic evaluation results showed that the G-MoMo applications’ interface designs lacked forward navigation buttons, uniformity in the applications’ menu titles, search fields, actions needed for recovery, and help and documentation. Similarly, the usability testing revealed that they were easy to learn, effective, efficient, memorable, with few errors, subscriber satisfaction, easy to use, aesthetic, easy to integrate, and understandable. Implementing a secure mobile money authentication and authorisation by combining multiple factors which are securely stored helps mobile money subscribers and other stakeholders to have trust in the developed native G-MoMo applications

ATHENA Research Book, Volume 1

The ATHENA European University is an alliance of nine Higher Education Institutions with the mission of fostering excellence in research and innovation by facilitating international cooperation. The ATHENA acronym stands for Advanced Technologies in Higher Education Alliance. The partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal, and Slovenia: the University of Orléans, the University of Siegen, the Hellenic Mediterranean University, the Niccolò Cusano University, the Vilnius Gediminas Technical University, the Polytechnic Institute of Porto, and the University of Maribor. In 2022 institutions from Poland and Spain joined the alliance: the Maria Curie-Skłodowska University and the University of Vigo. This research book presents a selection of the ATHENA university partners' research activities. It incorporates peer-reviewed original articles, reprints and student contributions. The ATHENA Research Book provides a platform that promotes joint and interdisciplinary research projects of both advanced and early-career researchers

Ultra-fast Imaging of Two-Phase Flow in Structured Monolith Reactors; Techniques and Data Analysis

This thesis will address the use of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques to probe the “monolith reactor”, which consists of a structured catalyst over which reactions may occur. This reactor has emerged as a potential alternative to more traditional chemical engineering systems such as trickle bed and slurry reactors. However, being a relatively new design, its associated flow phenomena and design procedures are not rigorously understood, which is retarding its acceptance in industry. Traditional observations are unable to provide the necessary information for design since the systems are opaque and dynamic. Therefore, NMR is proposed as an ideal tool to probe these systems in detail. The theory of NMR is summarised and the development of novel NMR techniques is presented. Novel techniques are validated in simple systems, and tested in more complex systems to ascertain their quantitative nature, and to find their limitations. These techniques are improvements over existing techniques in that they either decrease the acquisition time (allowing the observation of dynamically-changing systems) or allow us to probe systems in different ways to extract useful information. The goal of this research is to better understand the flow phenomena present in such systems, and to use this information to design better, more efficient, more controllable industrial reactors. The analysis of the NMR data acquired is discussed in detail, and several novel image-processing techniques have been developed to aid in the quantification of features within the images, and also to measure quantities such as holdup and velocity. These novel techniques are validated, and then applied to the systems of interest. Various configurations of monolith reactor, ranging from low flow rate systems to more challenging (and more industrially relevant) turbulent systems, are probed using these methods, and the contrasting flow phenomena and performance of these systems are discussed, with a view to optimisation of the choice of design parameters