Iris recognition systems capture an image from an individual's eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or normalized properly. This thesis is to enhance the performance of segmentation and normalization processes in iris recognition systems to increase the overall accuracy. <br /><br /> The previous iris segmentation approaches assume that the boundary of pupil is a circle. However, according to our observation, circle cannot model this boundary accurately. To improve the quality of segmentation, a novel active contour is proposed to detect the irregular boundary of pupil. The method can successfully detect all the pupil boundaries in the CASIA database and increase the recognition accuracy. <br /><br /> Most previous normalization approaches employ polar coordinate system to transform iris. Transforming iris into polar coordinates requires a reference point as the polar origin. Since pupil and limbus are generally non-concentric, there are two natural choices, pupil center and limbus center. However, their performance differences have not been investigated so far. We also propose a reference point, which is the virtual center of a pupil with radius equal to zero. We refer this point as the linearly-guessed center. The experiments demonstrate that the linearly-guessed center provides much better recognition accuracy. <br /><br /> In addition to evaluating the pupil and limbus centers and proposing a new reference point for normalization, we reformulate the normalization problem as a minimization problem. The advantage of this formulation is that it is not restricted by the circular assumption used in the reference point approaches. The experimental results demonstrate that the proposed method performs better than the reference point approaches. <br /><br /> In addition, previous normalization approaches are based on transforming iris texture into a fixed-size rectangular block. In fact, the shape and size of normalized iris have not been investigated in details. In this thesis, we study the size parameter of traditional approaches and propose a dynamic normalization scheme, which transforms an iris based on radii of pupil and limbus. The experimental results demonstrate that the dynamic normalization scheme performs better than the previous approaches.

Iris recognition systems capture an image from an individual's eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or normalized properly. This thesis is to enhance the performance of segmentation and normalization processes in iris recognition systems to increase the overall accuracy. <br /><br /> The previous iris segmentation approaches assume that the boundary of pupil is a circle. However, according to our observation, circle cannot model this boundary accurately. To improve the quality of segmentation, a novel active contour is proposed to detect the irregular boundary of pupil. The method can successfully detect all the pupil boundaries in the CASIA database and increase the recognition accuracy. <br /><br /> Most previous normalization approaches employ polar coordinate system to transform iris. Transforming iris into polar coordinates requires a reference point as the polar origin. Since pupil and limbus are generally non-concentric, there are two natural choices, pupil center and limbus center. However, their performance differences have not been investigated so far. We also propose a reference point, which is the virtual center of a pupil with radius equal to zero. We refer this point as the linearly-guessed center. The experiments demonstrate that the linearly-guessed center provides much better recognition accuracy. <br /><br /> In addition to evaluating the pupil and limbus centers and proposing a new reference point for normalization, we reformulate the normalization problem as a minimization problem. The advantage of this formulation is that it is not restricted by the circular assumption used in the reference point approaches. The experimental results demonstrate that the proposed method performs better than the reference point approaches. <br /><br /> In addition, previous normalization approaches are based on transforming iris texture into a fixed-size rectangular block. In fact, the shape and size of normalized iris have not been investigated in details. In this thesis, we study the size parameter of traditional approaches and propose a dynamic normalization scheme, which transforms an iris based on radii of pupil and limbus. The experimental results demonstrate that the dynamic normalization scheme performs better than the previous approaches.

Master of Science / Department of Electrical and Computer Engineering / D. V. Satish Chandra / Biometric technologies are the foundation of personal identification systems. A biometric system recognizes an individual based on some characteristics or processes. Characteristics used for recognition include features measured from face, fingerprints, hand geometry, handwriting, iris, retina, vein, signature and voice. Among the various techniques, iris recognition is regarded as the most reliable and accurate biometric recognition system. However, the technology of iris coding is still at an early stage. Iris recognition system consists of a segmentation system that localizes the iris region in an eye image and isolates eyelids, eyelashes. Segmentation is achieved using circular Hough transform for localizing the iris and pupil regions, linear Hough transform for localizing the eyelids and thresholding for detecting eyelashes. The segmented iris region is normalized to a rectangular block with fixed polar dimensions using Daugman’s rubber sheet model. The work presented in this report involves extraction of iris templates using the algorithms developed by Daugman. Features are then extracted from these templates using wavelet transform to perform the recognition task. Method of extracting features using cumulative sums is also investigated. Iris codes are generated for each cell by computing cumulative sums which describe variations in the grey values of iris. For determining the performance of the proposed iris recognition systems, CASIA database and UBRIS.v1 database of digitized grayscale eye images are used. K-nearest neighbor and Hamming distance classifiers are used to determine the similarity between the iris templates. The performance of the proposed methods is evaluated and compared.

In recent years, computing power and biometric sensors have not only become more powerful, but also more affordable to the general public. In turn, there has been great interest in developing and deploying biometric personal ID systems. Unlike the conventional security systems that often require people to provide artificial identification for verification, i.e. password or algorithmic generated keys, biometric security systems use an individual's biometric measurements, including fingerprint, face, hand geometry, and iris. It is believed that these measurements are unique to the individual, making them much more reliable and less likely to be stolen, lost, forgotten, or forged. Among these biometric measurements, the iris is regarded as one of the most reliable and accurate security approaches because it is an internal organ protected by the body's own biological mechanisms. It is easy to access, and almost impossible to modify without the risk of damaging the iris. Although there have been significant advancements in developing iris-based identification processes during recent years, there remains significant room for improvement. This dissertation presents a novel approach to the iris localization and code matching. It uses a fixed diameter method and a parabolic curve fitting approach for locating the iris and eyelids as well as a k-d tree for iris matching. The iris recognition rate is improved by accurately locating the eyelids and eliminating the signal noise in an eye image. Furthermore, the overall system performance is increased significantly by using a partial iris image and taking the advantage of the k-d binary tree. We present the research results of four processing stages of iris recognition: localization, normalization, feature extraction, and code matching. The localization process is based on histogram analysis, morphological process, Canny edge detection, and parabolic curve fitting. The normalization process adopts Daugman's rubber-sheet approach and converts the iris image from Cartesian coordinators to polar coordinates. In the feature extraction process, the feature vectors are created and quantized using 1-D Log-Gabor wavelet. Finally, the iris code matching process is conducted using a k-dimensional binary tree and Hamming distance.

A textura da íris humana é uma das peculiaridades biométricas mais confiáveis, pois os padrões que compõem sua estrutura são considerados únicos e estáveis por longos anos. No entanto, amostras de íris capturadas em ambiente não cooperativo como reconhecimento de íris a distância, por exemplo, estão sujeitas a conter variações na textura, devido a mudanças comportamentais da membrana da íris. Outro problema é a complexidade do algoritmo, que o torna inviável para aplicações práticas ou em tempo real. O objetivo deste trabalho foi avaliar alguns descritores de textura locais para o reconhecimento biométrico de íris, considerando os efeitos de dilatação e contração da pupila. Para a comprovação da hipótese desta tese de doutoramento, foi utilizada uma base de dados contendo amostras de íris com a pupila contraída e dilatada, simulando assim, a aquisição natural em ambiente não cooperativo. Além disso, foram propostos dois novos descritores, denominados como Median Local Mapped Pattern (Median-LMP) e Modified Median Local Mapped Pattern (MM-LMP), que foram comparados com o método de Daugman, o Local Mapped Pattern (LMP), o Completed Modeling of Local Binary Pattern (CLBP), o Median Binary Pattern (MBP) e o Weber Law Descriptor (WLD). Os resultados da avaliação de desempenho mostraram que o algoritmo de Daugman é o melhor para o reconhecimento de íris quando é realizada a comparação entre amostras de íris com pupilas contraídas. No entanto, se a pupila está dilatada, os descritores propostos apresentaram o melhor desempenho, principalmente se uma amostra de íris com uma pupila contraída é comparada com outra íris com a pupila dilatada. Além disso, os descritores propostos e o LMP obtiveram os menores tempos de processamento, sendo mais adequados do que os demais para aplicações em tempo preditivo com implementação em hardware. / The texture of the human iris is one of the most reliable biometric traits, so the patterns that make up its structure are the only criteria and stable for long time. However, iris samples captured in a noncooperative environment as recognition of nature, for example, subject to contain variations in texture, due to behavioral changes of the iris membrane. Another problem is an algorithm complexity, which makes it impractical for practical or in real-time applications. The objective of this work is to evaluate some local texture descriptors for the biometric iris recognition, considering the effects of dilation and contraction of the pupil. In order to prove the hypothesis of this doctoral question, a database was used containing iris samples with a contracted and dilated pupil, thus simulating a natural acquisition in a noncooperative environment. In addition, two new descriptors, named Median-Local Standard Mapped (Median-LMP) and Modified Modified Local Standard Mapped (MM-LMP) were proposed, which were compared with the Daugman method, the Mapped Local Pattern (LMP), the Complete Local Binary Pattern Modeling (CLBP), the Median Binary Standard (MBP) and Weber Law Descriptor (WLD). The results of the performance evaluation show that the Daugman algorithm is the best for iris recognition when a study of iris samples with the students is performed. However, if a pupil is dilated, the proposed descriptors show the best performance, especially a sample of iris with a contracted pupil is compared to another iris with a dilated pupil. In addition, the proposed descriptors and the LMP obtained the shortest processing times, being more adequate than the others for predictive time applications with hardware implementation.

A textura da íris humana é uma das peculiaridades biométricas mais confiáveis, pois os padrões que compõem sua estrutura são considerados únicos e estáveis por longos anos. No entanto, amostras de íris capturadas em ambiente não cooperativo como reconhecimento de íris a distância, por exemplo, estão sujeitas a conter variações na textura, devido a mudanças comportamentais da membrana da íris. Outro problema é a complexidade do algoritmo, que o torna inviável para aplicações práticas ou em tempo real. O objetivo deste trabalho foi avaliar alguns descritores de textura locais para o reconhecimento biométrico de íris, considerando os efeitos de dilatação e contração da pupila. Para a comprovação da hipótese desta tese de doutoramento, foi utilizada uma base de dados contendo amostras de íris com a pupila contraída e dilatada, simulando assim, a aquisição natural em ambiente não cooperativo. Além disso, foram propostos dois novos descritores, denominados como Median Local Mapped Pattern (Median-LMP) e Modified Median Local Mapped Pattern (MM-LMP), que foram comparados com o método de Daugman, o Local Mapped Pattern (LMP), o Completed Modeling of Local Binary Pattern (CLBP), o Median Binary Pattern (MBP) e o Weber Law Descriptor (WLD). Os resultados da avaliação de desempenho mostraram que o algoritmo de Daugman é o melhor para o reconhecimento de íris quando é realizada a comparação entre amostras de íris com pupilas contraídas. No entanto, se a pupila está dilatada, os descritores propostos apresentaram o melhor desempenho, principalmente se uma amostra de íris com uma pupila contraída é comparada com outra íris com a pupila dilatada. Além disso, os descritores propostos e o LMP obtiveram os menores tempos de processamento, sendo mais adequados do que os demais para aplicações em tempo preditivo com implementação em hardware. / The texture of the human iris is one of the most reliable biometric traits, so the patterns that make up its structure are the only criteria and stable for long time. However, iris samples captured in a noncooperative environment as recognition of nature, for example, subject to contain variations in texture, due to behavioral changes of the iris membrane. Another problem is an algorithm complexity, which makes it impractical for practical or in real-time applications. The objective of this work is to evaluate some local texture descriptors for the biometric iris recognition, considering the effects of dilation and contraction of the pupil. In order to prove the hypothesis of this doctoral question, a database was used containing iris samples with a contracted and dilated pupil, thus simulating a natural acquisition in a noncooperative environment. In addition, two new descriptors, named Median-Local Standard Mapped (Median-LMP) and Modified Modified Local Standard Mapped (MM-LMP) were proposed, which were compared with the Daugman method, the Mapped Local Pattern (LMP), the Complete Local Binary Pattern Modeling (CLBP), the Median Binary Standard (MBP) and Weber Law Descriptor (WLD). The results of the performance evaluation show that the Daugman algorithm is the best for iris recognition when a study of iris samples with the students is performed. However, if a pupil is dilated, the proposed descriptors show the best performance, especially a sample of iris with a contracted pupil is compared to another iris with a dilated pupil. In addition, the proposed descriptors and the LMP obtained the shortest processing times, being more adequate than the others for predictive time applications with hardware implementation.

Identication of cartridge is very essential in the field of forensics, military or people who collect ammunitions. The cartridges can beidentied by their headstamps.This thesis presents work on identification and matching of cartridge headstamp from the image. The Libor Masek's open source iris recognition algorithm is considered for the identification of cartridge pattern from the image.The dataset is devoleped with the cartridge headstamp patterns and matching of cartridge headstamp patterns is implemented. For matching of the cartridge pattern the Hamming distance is considered as the metric to differentiate interclass and intraclass comparisons. Variance is used as a criteria to discard the unwanted areas of the cartridge headstamp pattern.Four distinct cartridge headstamp patterns are considered. Three cartridges of each headstamp pattern are considered for intra class comparisons. The validation of the method is performed.

This research focused on the evaluation of iris recognition on post-mortem subjects. It was to determine if iris image captures were suitable from post-mortem subjects and if the captures contained the features required to be used in recognition scenarios. One commercially available iris camera was used, the IriShield USB MK2120U. In order to complete this research, it was first necessary to obtain images from subjects that contain the proper features, including sharpness, pupil size, and image quality. The images were captured during three different conditions that would be possible to find under real-world circumstances. The first condition was as the decedent came into the coroner’s office before the vitreous fluid was sampled from the eyes. The second condition was after the vitreous fluid was sampled from the deceased. Sampling vitreous fluid is a common autopsy procedure. This second condition would also be similar to a subject with a punctured eye. The third condition was after replacing the volume of vitreous fluid with saline solution. Replacing the vitreous with saline restored the round shape to the eye. This study found that high quality images can be captured from a post-mortem eye and that matching images across conditions results in positive identification.<br>

Indiana University-Purdue University Indianapolis (IUPUI) / Biometrics identi es/veri es a person using his/her physiological or behavioral characteristics. It is becoming an important ally for law enforcement and homeland security. Among all the biometric modalities, iris is tested to be the most accurate one. However, most existing methods are not designed for non-cooperative users and cannot work with o -angle or low quality iris images. In this thesis, we propose a robust multi-stage feature extraction and matching approach for non-cooperative iris recognition. We developed the SURF-like method to extract stable feature points, used Gabor Descriptor method for local feature description, and designed the multi- stage feature extraction and matching scheme to improve the recognition accuracy and speed. The related experimental results show that the proposed method is very promising. In addition, two template security enhanced schemes for the proposed non- cooperative iris recognition are introduced. The related experimental results show that these two schemes can e ectively realize cancelability of the enrolled biometric templates while at the same time achieving high accuracy.

Made available in DSpace on 2016-06-02T19:05:57Z (GMT). No. of bitstreams: 1 4427.pdf: 8518956 bytes, checksum: 0179ef9750c36082852192a44b3e6834 (MD5) Previous issue date: 2012-06-14 / Financiadora de Estudos e Projetos / The identification of humans by their iris structure has been explored since 1993, when the first algorithm was made available by John Daugman. Since then, iris recognition systems are widely used for access control of several kinds of environments. Such systems typically requires the user´s cooperation, appropriate lighting conditions, and images obtained in the infra-red band. Dynamic methods for biometric identification has been the subject of studies in the past few years, including iris recognition in non-cooperative environments. This paper proposes a pre-processing methodology to enable iris images classification taken in a noncooperative setting, from users at a certain distance, or while moving. The methodology aims to select images from the visible band containing an acceptable level of noise, and as such being suitable to apply the classification algorithms. Experimental results have shown that images with up to 40% of noise can still be used, suggesting the methodology may be useful as an aid to implement iris recognition systems at distance or in motion. / A identificação de seres humanos pela estrutura da íris vem sendo explorada desde 1993, quando foi disponibilizado o primeiro algoritmo por John Daugman. Desde então, os sistemas de reconhecimento de íris são amplamente utilizados para o controle de acesso de diversas aplicações. Tais sistemas normalmente, requerem a cooperação do usuário, condições de iluminações adequadas, e imagens obtidas na banda infravermelha. Métodos dinâmicos para identificação biométrica tem sido objeto de estudo nos últimos anos, incluindo o reconhecimento de íris em ambientes não cooperativos. Este trabalho propõe uma metodologia de pré-processamento da imagens da íris para classificação de amostras capturadas de forma não cooperativa, a uma certa distância, ou em movimento pelo usuário. A metodologia visa selecionar imagens a partir da banda visível contendo um nível de ruído aceitável, de forma que possa ser eficaz na aplicação dos algoritmos de classificação. Resultados experimentais demostraram que imagens com até 40% de ruído podem ainda ser utilizadas, sugerindo a utilização da metodologia como um auxílio para implementação de sistemas de reconhecimento de íris à distância ou em movimento.