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.

The Internet has grown from its modest academic beginnings into an important, global communication medium. It has become a significant, intrinsic part of our lives, how we distribute information and how we transact. It is used for a variety of purposes, including: banking; home shopping; commercial trade - using EDI (Electronic Data Interchange); and to gather information for market research and other activities. Owing to its academic origins, the early developers of the Internet did not focus on security. However, now that it has rapidly evolved into an extensively used, global commercial transaction and distribution channel, security has become a big concern. Fortunately, the field of information security has started to evolve in response and is fast becoming an important discipline with a sound theoretical basis. The discipline views the twin processes of identification and authentication as crucial aspects of information security. An individual access attempt must be identifiable prior to access being authorised otherwise system confidentiality cannot be enforced nor integrity safeguarded. Similarly, non-denial becomes impossible to instigate since the system is unable to log an identity against specific transactions. Consequently, identification and authentication should always be viewed as the first step to successfully enforcing information security. The process of identification and authorisation is, in essence, the ability to prove or verify an identity. This is usually accomplished using either one or a combination of the following three traditional identification techniques: something you possess; something you know; or something you are. A critical consideration when designing an application is which identification method, or combination of methods, from the three described above to use. Each method offers its own pros and cons and there are many ways to compare and contrast them. The comparison made in this study identifies biometrics as the best solution in a distributed application environment. There are, however, two over-arching hindrances to its widespread adoption. The first is the environment’s complexity - with multiple applications being accessed by both the public and the private sectors - and the second is that not all biometrics are popular and no single method has universe appeal. The more significant hindrance of the two is the latter, that of acceptance and trust, because it matters little how good or efficient a system is if nobody is willing to use it. This observation suggests that the identification system needs to be made as flexible as possible. In a democratic society, it could be argued that the best way of ensuring the successful adoption of a biometric system would be to allow maximum freedom of choice and let users decide which biometric method they would like to use. Although this approach is likely to go a long way towards solving the acceptance issue, it increases the complexity of the environment significantly. This study attempts to solve this problem by reducing the environment’s complexity while simultaneously ensuring the user retains maximum biometric freedom of choice. This can be achieved by creating a number of central biometric repositories. Each repository would be responsible for maintaining a biometric template data store for a type of biometric. These repositories or “Biometric Authorities” would act as authentication facilitators for a wide variety of applications and free them from that responsibility. / Dissertation (MSc (Computer Engineering))--University of Pretoria, 2005. / Electrical, Electronic and Computer Engineering / MSc / unrestricted

Biometric identification uses individuals' characteristics to attempt to match a sample to a database of existing samples. An increasingly commonly used characteristic is the iris section of the eye, which is valued for its uniqueness among individuals and stability over time. One key concern with iris recognition systems is the time required to find a test sample's match in a database of subjects. This work considers methods for categorizing irises within a database, so that a search for a match to a test sample can be focused on the test sample's category. The main method for categorization used in this work is texton learning. Texton learning involves creating a vocabulary of features and determining how much of each feature a given sample has. Once images are represented by textons, they are clustered in an unsupervised process. Success of the system is assessed as its ability to take a previously unseen image from a subject and classify it the same as the database reference for the subject. This work improves upon the past applications of texton learning with more thorough experiments to determine the optimal number of textons and image clusters. This system also investigates different accuracy metrics, with this work detailing two key methods and their relative benefits. Additionally, more in depth analysis is given for potential time saving impacts for finding a database match. Beyond the improvements to texton learning, symbolic features (ethnicity and gender) have been incorporated into the categorization process using a probabilistic metric. This is an innovative combination of using the numerical representation of the iris along with demographic information.

Yes / Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.

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Iris recognition constitutes one of the most powerful method for the iden- tification and authentication of people today. This thesis aims to describe the algorithms used in a sophisticated and mathematically correct way, while re- maining comprehensible. The description of these algorithms is not the only objective of this thesis; the reason they were chosen and potential improvements or substitutions are also discussed. The background of iris recognition, its use in cryptosystems, and the application of error-correcting codes are investigated as well.

Iris recognition constitutes one of the most powerful method for the iden- tification and authentication of people today. This thesis aims to describe the algorithms used by a mathematical apparatus. The description of these algo- rithms is not the only objective of this thesis; the reason they were chosen and potential improvements or substitutions are also discussed. The background of iris recognition, its use in cryptosystems, and the application of error-correcting codes are investigated as well. The second version of the thesis eliminates errata and a quantum of inaccu- racies discovered in the first version, especially in the ROI Definition, the Hough Transform and the Feature Extraction sections. Besides that, it also contains se- veral new propositions. Last, but not least, it shows a potential implementation of the algorithms described by appending pseudocodes to the relevant sections. 1

Made available in DSpace on 2014-06-11T19:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-05-10Bitstream added on 2014-06-13T19:38:58Z : No. of bitstreams: 1 rogeri_jg_me_sjrp.pdf: 962940 bytes, checksum: 5f86f6439d28c1cc69d98e55069b9b90 (MD5) / Nos últimos anos, a segurança tornou-se uma preocupação constante da grande maioria das pessoas. Os sistemas biométricos vem ganhando destaque em soluções ligadas à segurança, uma vez que tratam de características físicas e comportamentais para reconhecimento dos indivíduos e permissões de acesso. Este trabalho objetivou a proposição e implementação de um método para reconhecimento de indivíduos por meio de características contidas na região interna da íris com um alto percentual de exatidão no reconhecimento e uma grande diminuição no tempo de processamento, se comparado aos demais métodos encontrados na literatura. No método proposto foram utilizados operadores de morfologia matemática para localização da íris, wavelet de log-Gabor para extração das características e a distância de Hamming para o reconhecimento. Os resultados experimentais obtidos utilizando a base de dados CASIA mostraram que o método é confiável e seguro, além de se destacar com relação ao baixo custo computacional / In the recent years, the security became a constant concern of most people. Biometric systems have been highlighted in solutions related to security, since they deal with physical and behavioral characteristics for individuals recognition and access permissions. This work aims at the implementation of a method for individuals recognition based on the characteristics of the inner region of the iris, seeking a high percentage of accuracy in the recognition and a great reduction in the processing time, as compared to other methods published so far. We use mathematical morphology to search the iris in the image, the log-Gabor wavelet for feature extraction and the Hamming distance for recognition. The experimental results obtained from CASIA database show that the method is safe and reliable, and stand out with regard to the low computational cost

Neste trabalho é apresentado um método para reconhecimento de seres humanos através da textura da íris. A imagem do olho é processada através da análise do gradiente, com uma técnica de dispersão aleatória de sementes. Um vetor de características é extraído para cada íris, baseado na análise dos componentes wavelet em diversos níveis de decomposição. Para se mensurar as distâncias entre esses vetores foi utilizado o cálculo da distância Euclidiana, gerando-se curvas recall x precision para se medir a eficiência do método desenvolvido. Os resultados obtidos com algumas famílias wavelets demonstraram que o método proposto é capaz de realizar o reconhecimento humano através da íris com uma precisão eficiente. / This work presents a method for recognition of human beings by iris texture. The image of the eye is processed through gradient analysis, based on a random dispersion of seeds. So, it is extracted a feature vector for each iris based on wavelet transform in some levels of decomposition. To estimate the distances between these vectors it was used the Euclidean distance, and recall x precision curves are generated to measure the efficiency of the developed method. The results gotten with some wavelets families had demonstrated that the proposed methodology is capable to do human recognition through the iris with an efficient precision.