Дисертації з теми "BIOMETRIC FRAMEWORK"

In the South African Social Security Agency (SASSA) Annual Report of 2011/2012 it is stated that there were more than 15 million social grants paid out to needy beneficiaries of which 10 927 731 were Child Support Grants. A major challenge that is continually being addressed is the management and administration of these grants. In particular, the focus is on service delivery and zero tolerance to fraud and corruption. SASSA has made various attempts to address these issues, such as the rollout of biometric smart cards in 2012. This research endeavour attempts to discover whether a framework can be designed where necessary factors are taken into consideration to provide for an efficient social grant application and delivery process that uses biometrics. The framework aims to suggest improvements in the use of biometrics for the social grants. Seeing that biometrics in this case is used as a technology to improve a system involving humans, this study followed a Design Science approach and made use of a case study to collect the data required for the study. Literature studies reviewed the fields of social grants and biometrics. The challenges and lessons learnt from current implementations of social grants and biometrics within the South African context and further abroad were also relevant for the study. The framework that resulted from the above was evaluated for validity and applicability after which a modified framework is presented. The research concludes with specific implementation guidelines as well as areas for future research.

Thesis (M.A. in Security Studies (Homeland Security and Defense))--Naval Postgraduate School, March 2010.
Thesis Advisor(s): Bergin, Richard ; Josefek, Robert. "March 2010." Description based on title screen as viewed on April 28, 2010. Author(s) subject terms: Arizona Criminal Justice Commission, biometrics technology, biometrics identification, facial recognition, fingerprint identification, law enforcement, information sharing, criminal information sharing, Arizona, Mexico, New Mexico, Texas, California, RISC, AFIS, IAFIS, NGI, governors border conferences, Central America, south west border initiative. Includes bibliographical references (p. 87-92). Also available in print.

This dissertation proposes the development of an identification verification model that can be implemented within the context of the Eastern Cape, South Africa in order to ensure that vulnerable children are provided with the requisite care that they deserve from the state. The dissertation provides both a developed and developing world perspective on the identification verification needs of vulnerable children by providing an overview of relevant South African policy with regard to caring for vulnerable children and presenting an international perspective with specific reference to current legislative developments in the United Kingdom and Malaysia. Chapter 1 provides a motivation for a framework to be used for the identification verification of children in developing countries by emphasising that the provision of basic social services to children is an urgent requirement for poverty eradication and is a necessity as documented in the United Nations Convention on the Rights of the Child. A background to the needs of vulnerable children in South Africa is given and the scope, limitations and research methodology used in the dissertation is presented. Chapter 2 provides an overview of child related policy in the South African Context both from a National Government and Eastern Cape perspective. Although extensive progress has been made in the development of policies aimed at protecting vulnerable children, the practical implementation of these policies has been hampered by numerous issues including the lack of coordination between key entities. Chapter 3 provides an introduction to several noteworthy international developments with regard to the identity verification of vulnerable children. Lessons learnt from identity verification systems from the United Kingdom and Malaysia are analyzed for applicability to the South African context. In addition to this, the use of biometric technology in identity verification systems and a number of biometric identification methodologies available are discussed. Chapter 4 proposes the development and implementation of a biometric identity verification model in the Eastern Cape Province of South Africa based on lessons learnt from the assessment of South African policy and international best practice. The system should be piloted in the Eastern Cape and, if successful, be implemented throughout South Africa with a possible view to future implementation on the African continent. The scope of the system, the technological requirements and a high level implementation plan together with the need to further research certain key aspects e.g. the cost implications are discussed. It is clear that the development of such a model and the implementation of such a system will ensure that vulnerable children are provided with the requisite care that they are constitutionally entitled to. Significant follow up research is required during the development of the model to ensure that all aspects of the model are well documented and during the implementation of the system to ensure that the requirements of the users both within the government and the general public are met.

We report on the design, implementation and performance issues of the DRUBIS (Distributed Rhodes University Biometric Identification System) experimentation framework. The Principal Component Analysis (PCA) face-recognition approach is used as a case study. DRUBIS is a flexible experimentation framework, distributed over a number of modules that are easily pluggable and swappable, allowing for the easy construction of prototype systems. Web services are the logical means of distributing DRUBIS components and a number of prototype applications have been implemented from this framework. Different popular PCA face-recognition related experiments were used to evaluate our experimentation framework. We extract recognition performance measures from these experiments. In particular, we use the framework for a more indepth study of the suitability of the DFFS (Difference From Face Space) metric as a means for image classification in the area of race and gender determination.

Ubiquitous access to patient medical records is an important aspect of caring for patient safety. Unavailability of sufficient medical information at the point-ofcare could possibly lead to a fatality. The U.S. Institute of Medicine has reported that between 44,000 and 98,000 people die each year due to medical errors, such as incorrect medication dosages, due to poor legibility in manual records, or delays in consolidating needed information to discern the proper intervention. In this research we propose employing emergent technologies such as Java SIM Cards (JSC), Smart Phones (SP), Next Generation Networks (NGN), Near Field Communications (NFC), Public Key Infrastructure (PKI), and Biometric Identification to develop a secure framework and related protocols for ubiquitous access to Electronic Health Records (EHR). A partial EHR contained within a JSC can be used at the point-of-care in order to help quick diagnosis of a patient’s problems. The full EHR can be accessed from an Electronic Health Records Centre (EHRC) when time and network availability permit. Moreover, this framework and related protocols enable patients to give their explicit consent to a doctor to access their personal medical data, by using their Smart Phone, when the doctor needs to see or update the patient’s medical information during an examination. Also our proposed solution would give the power to patients to modify the Access Control List (ACL) related to their EHRs and view their EHRs through their Smart Phone. Currently, very limited research has been done on using JSCs and similar technologies as a portable repository of EHRs or on the specific security issues that are likely to arise when JSCs are used with ubiquitous access to EHRs. Previous research is concerned with using Medicare cards, a kind of Smart Card, as a repository of medical information at the patient point-of-care. However, this imposes some limitations on the patient’s emergency medical care, including the inability to detect the patient’s location, to call and send information to an emergency room automatically, and to interact with the patient in order to get consent. The aim of our framework and related protocols is to overcome these limitations by taking advantage of the SIM card and the technologies mentioned above. Briefly, our framework and related protocols will offer the full benefits of accessing an up-to-date, precise, and comprehensive medical history of a patient, whilst its mobility will provide ubiquitous access to medical and patient information everywhere it is needed. The objective of our framework and related protocols is to automate interactions between patients, healthcare providers and insurance organisations, increase patient safety, improve quality of care, and reduce the costs.

Computer security is a growing field in the IT industry. One of the important aspects of the computer security is authentication. Using passwords (something you know) is one of the most common ways of authentications. But passwords have proven to provide weak level of security as they can be easily compromised. Some other ways of authenticating a user are using physical tokens, (something you possess) and biometrics, (something you are). Using any one of these techniques to secure a system always has its own set of threats. One way to make sure a system is secure is to use multiple factors to authenticate. One of the ways to use multiple factors is to use all the three factors of authentication, something you possess, something you are and something you know. This thesis discusses about different ways of authentication and implements a system using three factor authentication. It takes many security aspects of the system into consideration while implementing it, to make it secure.

The technology exists for the migration of healthcare data from its archaic paper-based system to an electronic one and once in digital form, to be transported anywhere in the world in a matter of seconds. The advent of universally accessible healthcare data benefits all participants, but one of the outstanding problems that must be addressed is how to uniquely identify and link a patient to his or her specific medical data. To date, a few solutions to this problem have been proposed that are limited in their effectiveness. We propose the use of biometric technology within our FIRD framework in solving the unique association of a patient to his or her medical data distinctively. This would allow a patient to have real time access to all of his or her recorded healthcare information electronically whenever it is necessary, securely with minimal effort, greater effectiveness, and ease.

Privacy of personal health records is of ultimate importance. Unfortunately, it is easy to obtain illegal access to electronic health records under insufficient security precautions. Access control based on token or username/password is not adequate for applications in health domain which require heightened security. Currently, electronic signature mechanisms are being employed as a strong alternative to classic methods. In addition, biometrics provide more precise results in comparison to electronic signature methods. However, applicability of biometrics in this field has been prohibited by factors such as the need for special hardware, increased implementation costs, and invasiveness of the biometry sensors (eg. iris topology, fingerprint). Behavioral biometrics such as speech, and keystroke dynamics are easier to implement, and do not suffer from the disadvantages mentioned for the static biometrics. Especially, using keystroke dynamics for user authentication is more advantageous than other advanced biometrics because the implementation is inexpensive and continuous identity control is plausible. The aim of this study is to show the feasibility of merging a biometry-based advanced identity verification method together with an initial access control procedure such as password check. In this study, we provide an authentication framework based on measuring similarity of the typing characteristics of medical reporters, while they are typing medical reports. We have made a prototype of the system and provided classification of keystroke timings for each operator. We have generated a testbed and measured similarity of typing patterns of 5 medical reporters upon typing 4 different kinds of medical reports. Our system performs with hundred percent accuracy in identifying the authorized operators from the reports they type. In current practice, electronic signatures are indispensable for health information systems, but our study shows that keystroke dynamics can easily be included in this chain for increased security.

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Emotion-based analysis has raised a lot of interest, particularly in areas such as forensics, medicine, music, psychology, and human-machine interface. Following this trend, the use of facial analysis (either automatic or human-based) is the most common subject to be investigated once this type of data can easily be collected and is well accepted in the literature as a metric for inference of emotional states. Despite this popularity, due to several constraints found in real world scenarios (e.g. lightning, complex backgrounds, facial hair and so on), automatically obtaining affective information from face accurately is a very challenging accomplishment. This work presents a framework which aims to analyse emotional experiences through naturally generated facial expressions. Our main contribution is a new 4-dimensional model to describe emotional experiences in terms of appraisal, facial expressions, mood, and subjective experiences. In addition, we present an experiment using a new protocol proposed to obtain spontaneous emotional reactions. The results have suggested that the initial emotional state described by the participants of the experiment was different from that described after the exposure to the eliciting stimulus, thus showing that the used stimuli were capable of inducing the expected emotional states in most individuals. Moreover, our results pointed out that spontaneous facial reactions to emotions are very different from those in prototypic expressions due to the lack of expressiveness in the latter.
Emotion-based analysis has raised a lot of interest, particularly in areas such as forensics, medicine, music, psychology, and human-machine interface. Following this trend, the use of facial analysis (either automatic or human-based) is the most common subject to be investigated once this type of data can easily be collected and is well accepted in the literature as a metric for inference of emotional states. Despite this popularity, due to several constraints found in real world scenarios (e.g. lightning, complex backgrounds, facial hair and so on), automatically obtaining affective information from face accurately is a very challenging accomplishment. This work presents a framework which aims to analyse emotional experiences through naturally generated facial expressions. Our main contribution is a new 4-dimensional model to describe emotional experiences in terms of appraisal, facial expressions, mood, and subjective experiences. In addition, we present an experiment using a new protocol proposed to obtain spontaneous emotional reactions. The results have suggested that the initial emotional state described by the participants of the experiment was different from that described after the exposure to the eliciting stimulus, thus showing that the used stimuli were capable of inducing the expected emotional states in most individuals. Moreover, our results pointed out that spontaneous facial reactions to emotions are very different from those in prototypic expressions due to the lack of expressiveness in the latter.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science, School of Computer Science and Applied Mathematics. May 2017.
Biometric features have been widely implemented to be utilized for forensic and civil applications. Amongst many different kinds of biometric characteristics, the fingerprint is globally accepted and remains the mostly used biometric characteristic by commercial and industrial societies due to its easy acquisition, uniqueness, stability and reliability. There are currently various effective solutions available, however the fingerprint identification is still not considered a fully solved problem mainly due to accuracy and computational time requirements. Although many of the fingerprint recognition systems based on minutiae provide good accuracy, the systems with very large databases require fast and real time comparison of fingerprints, they often either fail to meet the high performance speed requirements or compromise the accuracy. For fingerprint matching that involves databases containing millions of fingerprints, real time identification can only be obtained through the implementation of optimal algorithms that may utilize the given hardware as robustly and efficiently as possible. There are currently no known distributed database and computing framework available that deal with real time solution for fingerprint recognition problem involving databases containing as many as sixty million fingerprints, the size which is close to the size of the South African population. This research proposal intends to serve two main purposes: 1) exploit and scale the best known minutiae matching algorithm for a minimum of sixty million fingerprints; and 2) design a framework for distributed database to deal with large fingerprint databases based on the results obtained in the former item.
GR2018

M. Tech. Business Information Systems
Vehicle crime rate is increasing in many countries, particularly, in the developing countries. This includes theft and physical damages. These crimes are of major concern to individuals and organizations (such insurance companies). The crime rate on vehicles has impact on the country's economy. A more accurate identification processes and methods can help prevent crime, fraud, essential resources and reliable for access control. In the world today, car theft seems to grow every day and one of the best possible solution is to put in place high and accurate authorization and authentication method. The study investigated how Fingerprint Biometric Technology could be deployed in Motor vehicles to enhance the security features. This include investigation on the factors which influence and impact technology adoption. Factors influencing the deployment of Fingerprint Biometric Technology were identified from the findings and a Framework was developed. The primary aim of the Framework is to guide the adoption and use of Fingerprint Biometric Technology for an improved and enhanced security systems in Motor vehicles.

A seguinte dissertação foca-se na utilização dos vários sinais biométricos produzidos pelo ser humano (neste caso específico, os do jogador) para manipulação dos vários elementos existentes em um vídeo jogo. Estes elementos são constituídos pelas mecânicas de jogo, banda sonora, elementos visuais, inteligência artificial dos inimigos e personagens não jogáveis, diálogos da história e sequências da narrativa.Para tal, apresenta-se uma framework para o desenvolvimento de uma estrutura de suporte destinada a jogos que pretendam utilizar este tipo de mecanismo de interacção, bem como a implementação de um protótipo que utilize a mesma.
The following dissertation focuses on the use of several biometric signals produced by the human being (in this specific case, the player) to manipulate several elements present in a game. These elements are composed by the game mechanics, soundtrack, visual assets, enemies and NPCs' artificial intelligence, dialogues and narrative sequences.To do such, this work presents a framework for the development of a supporting structure meant for games that wish to use this type of interaction mechanism as well as the implementation of a prototype using the said framework.

A seguinte dissertação foca-se na utilização dos vários sinais biométricos produzidos pelo ser humano (neste caso específico, os do jogador) para manipulação dos vários elementos existentes em um vídeo jogo. Estes elementos são constituídos pelas mecânicas de jogo, banda sonora, elementos visuais, inteligência artificial dos inimigos e personagens não jogáveis, diálogos da história e sequências da narrativa.Para tal, apresenta-se uma framework para o desenvolvimento de uma estrutura de suporte destinada a jogos que pretendam utilizar este tipo de mecanismo de interacção, bem como a implementação de um protótipo que utilize a mesma.
The following dissertation focuses on the use of several biometric signals produced by the human being (in this specific case, the player) to manipulate several elements present in a game. These elements are composed by the game mechanics, soundtrack, visual assets, enemies and NPCs' artificial intelligence, dialogues and narrative sequences.To do such, this work presents a framework for the development of a supporting structure meant for games that wish to use this type of interaction mechanism as well as the implementation of a prototype using the said framework.

The automatic detection of abnormal events in surveillance footage is still a concern of the research community. Since protection is the primary purpose of installing video surveillance systems, the monitoring capability to keep public safety, and its rapid response to satisfy this purpose, is a significant challenge even for humans. Nowadays, human capacity has not kept pace with the increased use of surveillance systems, requiring much supervision to identify unusual events that could put any person or company at risk, without ignoring the fact that there is a substantial waste of labor and time due to the extremely low likelihood of occurring anomalous events compared to normal ones. Consequently, the need for an automatic detection algorithm of abnormal events has become crucial in video surveillance. Even being in the scope of various research works published in the last decade, the state-of-the-art performance is still unsatisfactory and far below the required for an effective deployment of this kind of technology in fully unconstrained scenarios. Nevertheless, despite all the research done in this area, the automatic detection of abnormal events remains a challenge for many reasons. Starting by environmental diversity, the complexity of movements resemblance in different actions, crowded scenarios, and taking into account all possible standard patterns to define a normal action is undoubtedly difficult or impossible. Despite the difficulty of solving these problems, the substantive problem lies in obtaining sufficient amounts of labeled abnormal samples, which concerning computer vision algorithms, is fundamental. More importantly, obtaining an extensive set of different videos that satisfy the previously mentioned conditions is not a simple task. In addition to its effort and time-consuming, defining the boundary between normal and abnormal actions is usually unclear. Henceforward, in this work, the main objective is to provide several solutions to the problems mentioned above, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns. Also, by exploring different strategies, we were able to develop new approaches that consistently advance the state-of-the-art performance. Moreover, we announce the availability of a new large-scale first of its kind dataset fully annotated at the frame level, concerning a specific anomaly detection event with a wide diversity in fighting scenarios, that can be freely used by the research community. Along with this document with the purpose of requiring minimal supervision, two different proposals are described; the first method employs the recent technique of self-supervised learning to avoid the laborious task of annotation, where the training set is autonomously labeled using an iterative learning framework composed of two independent experts that feed data to each other through a Bayesian framework. The second proposal explores a new method to learn an anomaly ranking model in the multiple instance learning paradigm by leveraging weakly labeled videos, where the training labels are done at the video-level. The experiments were conducted in several well-known datasets, and our solutions solidly outperform the state-of-the-art. Additionally, as a proof-of-concept system, we also present the results of collected real-world simulations in different environments to perform a field test of our learned models.
A detecção automática de eventos anómalos em imagens de videovigilância permanece uma inquietação por parte da comunidade científica. Sendo a proteção o principal propósito da instalação de sistemas de vigilância, a capacidade de monitorização da segurança pública, e a sua rápida resposta para satisfazer essa finalidade, é uma adversidade até para o ser humano. Nos dias de hoje, com o aumento do uso de sistemas de videovigilância, a capacidade humana não tem alcançado a cadência necessária, exigindo uma supervisão exorbitante para a identificação de acontecimentos invulgares que coloquem uma identidade ou sociedade em risco. O facto da probabilidade de se suceder um incidente ser extremamente reduzida comparada a eventualidades normais, existe um gasto substancial de tempo de ofício. Consequentemente, a necessidade para um algorítmo de detecção automática de incidentes tem vindo a ser crucial em videovigilância. Mesmo sendo alvo de vários trabalhos científicos publicados na última década, o desempenho do estado-da-arte continua insatisfatório e abaixo do requisitado para uma implementação eficiente deste tipo de tecnologias em ambientes e cenários totalmente espontâneos e incontinentes. Porém, apesar de toda a investigação realizada nesta área, a automatização de detecção de incidentes é um desafio que perdura por várias razões. Começando pela diversidade ambiental, a complexidade da semalhança entre movimentos de ações distintas, cenários de multidões, e ter em conta todos os padrões para definir uma ação normal, é indiscutivelmente difícil ou impossível. Não obstante a dificuldade de resolução destes problemas, o obstáculo fundamental consiste na obtenção de um número suficiente de instâncias classificadas anormais, considerando algoritmos de visão computacional é essencial. Mais importante ainda, obter um vasto conjunto de diferentes vídeos capazes de satisfazer as condições previamente mencionadas, não é uma tarefa simples. Em adição ao esforço e tempo despendido, estabelecer um limite entre ações normais e anormais é frequentemente indistinto. Tendo estes aspetos em consideração, neste trabalho, o principal objetivo é providenciar diversas soluções para os problemas previamente mencionados, concentrando na análise de métodos do estado-da-arte e apresentando uma visão abrangente dos mesmos para clarificar os conceitos aplicados na captura de padrões normais e anormais. Inclusive, a exploração de diferentes estratégias habilitou-nos a desenvolver novas abordagens que aprimoram consistentemente o desempenho do estado-da-arte. Por último, anunciamos a disponibilidade de um novo conjunto de dados, em grande escala, totalmente anotado ao nível da frame em relação à detecção de anomalias em um evento específico com uma vasta diversidade em cenários de luta, podendo ser livremente utilizado pela comunidade científica. Neste documento, com o propósito de requerer o mínimo de supervisão, são descritas duas propostas diferentes; O primeiro método põe em prática a recente técnica de aprendizagem auto-supervisionada para evitar a árdua tarefa de anotação, onde o conjunto de treino é classificado autonomamente usando uma estrutura de aprendizagem iterativa composta por duas redes neuronais independentes que fornecem dados entre si através de uma estrutura Bayesiana. A segunda proposta explora um novo método para aprender um modelo de classificação de anomalias no paradigma multiple-instance learning manuseando vídeos fracamente anotados, onde a classificação do conjunto de treino é feita ao nível do vídeo. As experiências foram concebidas em vários conjuntos de dados, e as nossas soluções superam consolidamente o estado-da-arte. Adicionalmente, como sistema de prova de conceito, apresentamos os resultados da execução do nosso modelo em simulações reais em diferentes ambientes.