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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.

Les performances des systèmes de reconnaissances basés sur l'iris sont très négativement affectées par les relâchements des contraintes lors de l'acquisition des images (sujet mobile ou faiblement coopératif, image acquise loin du capteur…). L’objectif de cette thèse est de proposer une amélioration des algorithmes de segmentation traditionnels afin de pouvoir travailler dans de telles conditions. Nous avons identifié et traité quarte modules qui permettent de limiter l'impact des dégradations des images sur les performances du système de reconnaissance global : • Une localisation précise et robuste de la pupille dans l'image l'œil. Pour cela, nous avons développé une méthode qui supprime les cils et les sourcils de l'image pour faciliter la détection de la pupille. • Une segmentation précise de la texture de l'iris dans l'image. Nous avons étudié plusieurs méthodes de la littérature des Contours Actifs et comparé l'impact de ces méthodes sur les performances de reconnaissances du système complet. • Une estimation précise et robuste des contours anatomique de l'iris indépendamment des occlusions dans l'image. Pour cela, nous avons dérivé les équations des Contours Actifs explicitement pour des cercles et des ellipses. Nous avons par ailleurs proposé une méthodologie efficace pour rendre la détection moins sensible aux minimas locaux. • Une méthode de détection des erreurs de segmentation. Il est en effet important de pouvoir avertir le système de reconnaissance global qu'une erreur s’est produite. Pour cela nous avons développé plusieurs critères d'évaluation de la qualité de segmentation. Nous avons ensuite fusionnés ces mesures en utilisant un algorithme de type <<Support Vector Regression>> (SVR) pour former une mesure de qualité globale évaluant la qualité de la segmentation / This thesis is focused on the development of robust segmentation algorithms for iris recognition systems working in degraded acquisition conditions. In controlled acquisition scenarios, iris segmentation is well handled by simple segmentation schemes, which modeled the iris borders by circles and assumed that the iris can only be occluded by eyelids. However, such simple models tend to fail when the iris is strongly occluded or off-angle, or when the iris borders are not sharp enough. In this thesis, we propose a complete segmentation system working efficiently despite the above-mentioned degradations of the input data. After a study of the recent state of the art in iris recognition, we identified four key issues that an iris segmentation system should handle when being confronted to images of poor quality, leading this way to four key modules for the complete system: • The system should be able locate the pupil in the image in order to initialize more complex algorithms. To address this problem, we propose an original and effective way to first segment dark elements in the image that can lead to mistakes of the pupil localization process. This rough segmentation detects high frequency areas of the image and then the system uses the pupil homogeneity as a criterion to identify the pupil area among other dark regions of the image. • Accurate segmentation of the iris texture in the eye image is a core task of iris segmentation systems. We propose to segment the iris texture by Active Contours because they meet both the requirement in robustness and accuracy required to perform segmentation on large databases of degraded images. We studied several Active Contours that varies in speed, robustness, accuracy and in the features they use to model the iris region. We make a comparative evaluation of the algorithms’ influence on the system performance. • A complete segmentation system must also accurately estimate the iris shape in occluded regions, in order to format the iris texture for recognition. We propose a robust and accurate scheme based on a variational formulation to fit an elliptic model on the iris borders. We explicitly derive evolution equations for ellipses using the Active Contours formalism. We also propose an effective way to limit the sensitivity of this process to initial conditions. This part of our work is currently under review for final acceptance in the international journal Computer Vision and Image Understanding (CVIU). • Finally, we address the main issue of automatic detection of segmentation failures of the system. Few works in the literature address measuring the quality of a segmentation algorithm, critical task for an operational system. We propose in this thesis a set of novel quality measures for segmentation and show a correlation between each of them with the intrinsic recognition performance of the segmented images. We perform fusion of the individual quality measures via a Support Vector Regression (SVR) algorithm, in order to propose a more robust global segmentation quality score

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Biometric identification systems have several distinct advantages over other authentication technologies, such as passwords, in reliably recognising individuals. Iris based recognition is one such biometric recognition system. Unlike other biometrics such as fingerprints or face images, the distinct aspect of the iris comes from its randomly distributed features. The patterns of these randomly distributed features on the iris have been proved to be fixed in a person's lifetime, and are stable over time for healthy eyes except for the distortions caused by the constriction and dilation of the pupil. The distortion of the iris pattern caused by pupillary activity, which is mainly due changes in ambient lighting conditions, can be significant. One important question that arises from this is: How closely do two different iris images of the same person, taken at different times using different cameras, in different environments, and under different lighting conditions, agree with each other? It is also problematic for iris recognition systems to correctly identify a person when his/her pupil size is very different from the person's iris images, used at the time of constructing the system's data-base. To date, researchers in the field of iris recognition have made attempts to address this problem, with varying degrees of success. However, there is still a need to conduct in-depth investigations into this matter in order to arrive at more reliable solutions. It is therefore necessary to study the behaviour of iris surface deformation caused by the change of lighting conditions. In this thesis, a study of the physiological behaviour of pupil size variation under different normal indoor lighting conditions (100 lux ~ 1,200 lux) and brightness levels is presented. The thesis also presents the results of applying Elastic Graph Matching (EGM) tracking techniques to study the mechanisms of iris surface deformation. A study of the pupil size variation under different normal indoor lighting conditions was conducted. The study showed that the behaviour of the pupil size can be significantly different from one person to another under the same lighting conditions. There was no evidence from this study to show that the exact pupil sizes of an individual can be determined at a given illumination level. However, the range of pupil sizes can be estimated for a range of specific lighting conditions. The range of average pupil sizes under normal indoor lighting found was between 3 mm and 4 mm. One of the advantages of using EGM for iris surface deformation tracking is that it incorporates the benefit of the use of Gabor wavelets to encode the iris features for tracking. The tracking results showed that the radial stretch of the iris surface is nonlinear. However, the amount of extension of iris surface at any point on the iris during the stretch is approximately linear. The analyses of the tracking results also showed that the behaviour of iris surface deformation is different from one person to another. This implies that a generalised iris surface deformation model cannot be established for personal identification. However, a deformation model can be established for every individual based on their analysis result, which can be useful for personal verification using the iris. Therefore, analysis of the tracking results of each individual was used to model iris surface deformations for that individual. The model was able to estimate the movement of a point on the iris surface at a particular pupil size. This makes it possible to estimate and construct the 2D deformed iris image of a desired pupil size from a given iris image of another different pupil size. The estimated deformed iris images were compared with their actual images for similarity, using an intensitybased (zero mean normalised cross-correlation). The result shows that 86% of the comparisons have over 65% similarity between the estimated and actual iris image. Preliminary tests of the estimated deformed iris images using an open-source iris recognition algorithm have showed an improved personal verification performance. The studies presented in this thesis were conducted using a very small sample of iris images and therefore should not be generalised, before further investigations are conducted.