This master thesis evaluates the use of off-the-shelf standard cameras for biometric identification of the human iris. As demands on secure identification are constantly rising and as the human iris provides with a pattern that is excellent for identification, the use of inexpensive equipment could help iris recognition become a new standard in security systems. To test the performance of such a system a review of the current state of the research in the area was done and the most promising methods were chosen for evaluation. A test environment based on open source code was constructed to measure the performance of iris recognition methods, image quality and recognition rate. In this paper the image quality of a database consisting of images from a standard camera is assessed, the most important problem areas identified, and the overall recognition performance measured. Iris recognition methods found in literature are tested on this class of images. These together with newly developed methods show that a system using standard equipment can be constructed. Tests show that the performance of such a system is promising.

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.

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.

Personal identity refers to a set of attributes (e.g., name, social insurance number, etc.) that are associated with a person. Identity management is the process of creating, maintaining and destroying identities of individuals in a population. Biometric technologies are technologies developed to use statistical analysis of an individual’s biological or behavioral traits to determine his identity. Biometrics based authentication systems offer a reliable solution for identity management, because of their uniqueness, relative stability over time and security (among other reasons). Public acceptance of biometric systems will depend on their ability to ensure robustness, accuracy and security. Although robustness and accuracy of such systems are rapidly improving, there still remain some issues of security and balancing it with privacy. While the uniqueness of biometric traits offers a convenient and reliable means of identification, it also poses the risk of unauthorized cross-referencing among databases using the same biometric trait. There is also a high risk in case of a biometric database being compromised, since it’s not possible to revoke the biometric trait and re-issue a new one as is the case with passwords and smart keys. This unique attribute of biometric based authentication system poses a challenge that might slow down public acceptance and the use of biometrics for authentication purposes in large scale applications. In this research we investigate the vulnerabilities of biometric systems focusing on template security in iris-based biometric recognition systems. The iris has been well studied for authentication purposes and has been proven accurate in large scale applications in several airports and border crossings around the world. The most widely accepted iris recognition systems are based on Daugman’s model that creates a binary iris template. In this research we develop different systems using watermarking, bio-cryptography as well as feature transformation to achieve revocability and security of binary templates in iris based biometric authentication systems, while maintaining the performance that enables widespread application of these systems. All algorithms developed in this research are applicable on already existing biometric authentication systems and do not require redesign of these existing, well established iris-based authentication systems that use binary templates.

M.Sc. (Computer Science) / The past couple of years have seen an explosion in the number of online fraud schemes – Total annual losses exceed tens of millions of Rands. Many people and organizations from all over the world have fallen victim to it. Nobody is safe; everybody is vulnerable. As we increasingly make use of the Internet and mobile technology to do our work and to perform chores such as online banking or shopping, we become even more vulnerable. Fraudsters make use of ever-more sophisticated techniques and clever schemes to target the unsuspecting end-users of mobile and Internet technology, and to trick them into surrendering their personal information so that electronic transactions can be carried out in their name. Examples include cheque and credit card fraud, phishing, identity theft and spyware, to name but a few. Services such as Internet- and cell phone banking especially, provide a haven of possibilities to make easy money because the technology is relatively new, and it is being used by evermore people who believe that it is fast, safe and secure and will therefore make their lives easier. One of the latest scams at the time of this writing is “SIM swap fraud”: Fraudsters target specific victims, perform SIM swaps on their cell phone numbers, and then proceed to empty the victims’ bank accounts. This is all done in a matter of minutes, so the victims only realize what has happened when it is too late to do anything about it. Needless to say, a solution must be found that will prevent unauthorized SIM swaps and strengthen online banking security. This dissertation does exactly that. It examines the digital world known as cyberspace, identifies how SIM swap fraudsters manage to defraud their targets, and presents a biometrics-based security system to combat SIM swap fraud.

Orientadores: Alexandre Xavier Falcão, Giovani Chiachia / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-27T14:41:07Z (GMT). No. of bitstreams: 1 Martins_SamuelBotter_M.pdf: 4782261 bytes, checksum: 63cd58756e3fe70ffe625d42974b1a78 (MD5) Previous issue date: 2015 / Resumo: Sistemas automáticos de reconhecimento de faces tem atraído a atenção da indústria e da academia, devido à gama de possíveis aplicações, tais como vigilância, controle de acesso, etc. O recente progresso em tais sistemas motiva o uso de técnicas de aprendizado em profundidade e classificadores específicos para cada usuário em cenários de operação não-controlado, que apresentam variações consideráveis em pose, iluminação, etc. Sistemas automáticos de reconhecimento de faces possibilitam construir bases de imagens anotadas por meio do processo de cadastramento de novos usuários. Porém, à medida que as bases de dados crescem, torna-se crucial reduzir o número de amostras negativas usadas para treinar classificadores específicos para cada usuário, devido às limitações de processamento e tempo de resposta. Tal processo de aprendizado discriminativo durante o cadastramento de novos indivíduos tem implicações no projeto de sistemas de reconhecimento de faces. Apesar deste processo poder aumentar o desempenho do reconhecimento, ele também pode afetar a velocidade do cadastramento, prejudicando, assim, a experiência do usuário. Neste cenário, é importante selecionar as amostras mais informativas buscando maximizar o desempenho do classificador. Este trabalho resolve tal problema propondo um método de aprendizado discriminativo durante o cadastramento de usuários com o objetivo de não afetar a velocidade e a confiabilidade do processo. Nossa solução combina representações de alta dimensão com um algoritmo que rapidamente minera imagens faciais negativas de um conjunto de minerção grande para assim construir um classificador específico para cada usuário, baseado em máquinas de vetores de suporte. O algoritmo mostrou ser robusto em construir pequenos e eficazes conjuntos de treinamento com as amostras negativas mais informativas para cada indivíduo. Avaliamos nosso método em duas bases contendo imagens de faces obtidas no cenário de operação não-controlado, chamadas PubFig83 e Mobio, e mostramos que nossa abordagem é capaz de alcançar um desempenho superior em tempos interativos, quando comparada com outras cinco abordagens consideradas. Os resultados indicam que o nosso método tem potencial para ser explorado pela indústria com mínimo impacto na experiência do usuário. Além disso, o algoritmo é independente de aplicação, podendo ser uma contribuição relevante para sistemas biométricos que visam manter a robustez à medida que o número de usuários aumenta / Abstract: Automatic face recognition has attracted considerable attention from the industry and academy due to its wide range of applications, such as video surveillance, access control, online transactions, suspect identification, etc. The recent progress in face recognition systems motivates the use of deep learning techniques and user-specific face representation and classification models for unconstrained scenarios, which present considerable variations in pose, face appearance, illumination, etc. Automatic face recognition systems make possible to build annotated face datasets through user enrollment. However, as the face datasets grow, it becomes crucial to reduce the number of negative samples used to train user-specific classifiers, due to processing constraints and responsiveness. Such a discriminative learning process during the enrollment of new individuals has implications in the design of face recognition systems. Even though it might increase recognition performance, it may affect the speed of the enrollment, which in turn may affect the user experience. In this scenario, it is important to select the most informative samples in order to maximize the performance of the classifier. This work addresses this problem by proposing a discriminative learning method during user enrollment with the challenges of not negatively affecting the speed and reliability of the process, and so the user experience. Our solution combines high-dimensional representations from deep learning with an algorithm for rapidly mining negative face images from a large mining set to build an effective classification model based on linear support vector machines for each specific user. The negative mining algorithm has shown to be robust in building small and effective training sets with the most informative negative samples for each given individual. We evaluate our approach on two unconstrained datasets, namely PubFig83 and Mobio, and show that it is able to attain superior performance, within interactive response times, as compared to five other baseline approaches that use the same classification scheme. The results indicate that our approach has potential to be exploited by the industry with minimum impact to the user experience. Moreover, the algorithm is application-independent. Hence, it may be a relevant contribution for biometric systems that aim to maintain robustness as the number of users increases / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

The issue of the use of biometric data in labour relations Abstract This diploma thesis deals with the current topic of the legal framework for the protection of biometric data in the context of labour relations. With the development of information technology, interest in the use of biometric data is growing. It is becoming common in the workplace that the biometric data are used, among others, for recording attendance, gaining access either to the workplace itself or to the individual work equipment. Their use is simple and convenient, as employees can use for verification their fingerprints, iris scans, etc. However, biometric data are inherently unchangeable, which is an advantage as well as the Achilles heel of their processing. Therefore, due to their nature, they fall into the category of sensitive personal data and as such enjoy enhanced protection. Development in the field of information technology suggests that biometric data are highly likely to become part of everyday life. Hence, it is more than appropriate for this development to be reflected in the legislation. The aim of this diploma thesis is to provide a general insight into the issue of using biometric data in the labour relations, where a particularly high increase in their use can be expected. The first part of this thesis acquaints the...

The aim of the thesis is to provide complete information regarding electronic documents and possibilities of their usage. The focus is concentrated on the area of authentication, which specifies the possibility of obtaining authentication information and describes the authentication processes itself. The diploma thesis also deals with the suggestion of multifactor authentication of electronic documents for the selected company.

This diploma thesis focuses on multibiometric systems, specifically on biometric fusion. The thesis describes eye biometrics, i.e. recognition based on retina and iris. The key part consists of design and implementation specification of a biometric system based on retina and iris recognition.

This paper is about security of the RFID systems, attacks on them and countermeasures. Attack to obtain secret key from Mifare Classic card was implemented. Options for eavesdropping RFID communication, security of the NFC technology and biometric passports are descibed too.