Academic literature on the topic 'Iris pattern'

Iris recognition used the iris features to verify and identify the identity of human. The iris has many advantages such as stability over time, easy to use and high recognition accuracy. However, the poor quality of iris images can degrade the recognition accuracy of iris recognition system. The recognition accuracy of this system is depended on the iris pattern quality captured during the iris acquisition. The iris pattern quality can degrade due to the blurry image. Blurry image happened due to the movement during image acquisition and poor camera resolution. Due to that, a deblurring method based on the Wiener filter was proposed to improve the quality of iris pattern. This work is significant since the proposed method can enhance the quality of iris pattern in the blurry image. Based to the results, the proposed method improved the quality of iris pattern in the blurry image. Moreover, it recorded the fastest execution time to improve the quality of iris pattern compared to the other methods.

Iris recognition used the iris features to verify and identify the identity of human. The iris has many advantages such as stability over time, easy to use and high recognition accuracy. However, the poor quality of iris images can degrade the recognition accuracy of iris recognition system. The recognition accuracy of this system is depended on the iris pattern quality captured during the iris acquisition. The iris pattern quality can degrade due to the blurry image. Blurry image happened due to the movement during image acquisition and poor camera resolution. Due to that, a deblurring method based on the Wiener filter was proposed to improve the quality of iris pattern. This work is significant since the proposed method can enhance the quality of iris pattern in the blurry image. Based to the results, the proposed method improved the quality of iris pattern in the blurry image. Moreover, it recorded the fastest execution time to improve the quality of iris pattern compared to the other methods.

Iris merupakan bagian dari mata yang memiliki keunikan. Keunikan pada iris ini menjadi alasan iris digunakan sebagai identitas seperti sidik jari,dan suara. Dibandingkan dengan sidik jari, iris memiliki kelebihan karena letak iris yang lebih terlindungi. Setiap individu memiliki pola iris yang berbeda dan pembentukan pola iris tidak berhubungan dengan faktor genetik individu, sehingga iris merupakan biometrik yang memiliki keunikan yang tinggi dan sulitnya untuk dilakukan pemalsuan biometrik. Identifikasi atau pengenalan iris dilakukan dengan menggunakan citra iris. Pada penelitian ini citra iris akan dilakukan tahap praproses yaitu dengan menghilangkan noise seperti bulu dan kelopak mata, yang kemudian hasil praproses citra iris dilakukan ekstraksi fitur menggunakan algoritma Local Binary Pattern (LBP). Setelah proses ekstraksi fitur dilakukan, proses selanjutnya adalah melakukan pelatihan menggunakan Radial Basis Function (RBF). Setelah proses pelatihan, model RBF diuji dengan data iris yang berbeda. Akurasi tertinggi yang dicapai pada pengenalan iris menggunakan fitur LBP dan RBF yaitu 83,33%.

Newly invented Iris recognition which is a part of biometric identification,offering and purposing an antique method for personal identification, authentication and security by analyzing the random pattern of the iris. By using iris recognition system recognizes the identification of a person from a captured image by comparing it to the human iris patterns stored in an iris template database. The iris template database has been carried out by using three steps the first step is segmentation. Hough transform is used to segment the iris region from the eye image of the CASIA database. The noise and blurring due to eyelid occlusions, reflections is eliminated in the segmentation stage. The third step is normalization. A technique based on Hough Transform was employed on the iris for creating a dimensionally steady and compatible representation of the iris region. The last step and fourth step is feature extraction. In this Local Binary Pattern and Gray level Cooccurrence Matrix are used to extract the features. At last template of the new eye image captured will be compared with the iris template database using Probabilistic Neural Network.

Human iris is a good subject of biometrical identification, since iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in particular artificial neural networks.For iris recognition problem solving Kohenen Self Organizing Maps are suggested to use. The software for iris recognition is developed which is customizable and allows to select the appropriate parameters of the neural network to obtain the most satisfactory results. The developed Self-Organizing Map Library of classes can be used for various kinds of object classification problem solving as well as for any problems suitable to solve with Self-Organizing Maps.

Abstract Renal transplant recipients (RTRs) are at increased risk of keratinocyte skin cancers, with a tendency to have multiple, aggressive and difficult-to-treat tumours. The eye and the skin share the same embryological ectoderm. Iris pattern has recently been reported as a predictive risk factor for skin cancer in nonimmunosuppressed Southern European and Irish populations. Our aim was to analyse whether an individual’s iris pattern is an independent risk factor for the development of keratinocyte carcinoma in RTRs. Iris patterns of 110 RTRs were evaluated using the Simionescu visual three-step technique (iris periphery, collarette and iris freckling). Established risk factors for skin cancer in patients with transplants were recorded as confounding factors. This was an observational cross-sectional study. Among the 110 RTRs, 31 participants had skin cancer. In the skin cancer group, iris periphery was blue/grey in 74% (P = 0.053, odds ratio 2.5), the collarette was light brown in 57% (P < 0.004) and iris freckles were present in 55% (P = 0.04). Dark brown and blue collarettes were observed in controls. Binary logistic regression analysis showed that light brown collarette is a significant independent risk factor for skin cancer (odds ratio 4.54, confidence interval 1.56–10.6, P = 0.02). Within this RTR population a blue iris periphery, light brown collarette and presence of freckling confer an independent risk for skin cancer. Iris pattern is a useful tool for identification of transplant patients at risk of skin cancer and an easy-to-use technique for risk evaluation in this cohort. This is the first study to investigate iris pattern and skin cancer risk in RTRs.

In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman’s to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.

Abstract A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson’s correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises.

This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition accuracy of the-state-of-the-art systems decreases significantly when these constraints are relaxed. Recent advances have proposed different methods to process iris images captured in unconstrained environments. While these methods improve the accuracy of the original iris recognition system, they still have segmentation and feature selection problems, which results in high FRR (False Rejection Rate) and FAR (False Acceptance Rate) or in recognition failure. In the first part of this thesis, a novel segmentation algorithm for detecting the limbus and pupillary boundaries of human iris images with a quality assessment process is proposed. The algorithm first searches over the HSV colour space to detect the local maxima sclera region as it is the most easily distinguishable part of the human eye. The parameters from this stage are then used for eye area detection, upper/lower eyelid isolation and for rotation angle correction. The second step is the iris image quality assessment process, as the iris images captured under unconstrained conditions have heterogeneous characteristics. In addition, the probability of getting a mis-segmented sclera portion around the outer ring of the iris is very high, especially in the presence of reflection caused by a visible wavelength light source. Therefore, quality assessment procedures are applied for the classification of images from the first step into seven different categories based on the average of their RGB colour intensity. An appropriate filter is applied based on the detected quality. In the third step, a binarization process is applied to the detected eye portion from the first step for detecting the iris outer ring based on a threshold value defined on the basis of image quality from the second step. Finally, for the pupil area segmentation, the method searches over the HSV colour space for local minima pixels, as the pupil contains the darkest pixels in the human eye. In the second part, a novel discriminating feature extraction and selection based on the Curvelet transform are introduced. Most of the state-of-the-art iris recognition systems use the textural features extracted from the iris images. While these fine tiny features are very robust when extracted from high resolution clear images captured at very close distances, they show major weaknesses when extracted from degraded images captured over long distances. The use of the Curvelet transform to extract 2D geometrical features (curves and edges) from the degraded iris images addresses the weakness of 1D texture features extracted by the classical methods based on textural analysis wavelet transform. Our experiments show significant improvements in the segmentation and recognition accuracy when compared to the-state-of-the-art results.

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.

Nature has created complex biological structures that exhibit intelligent behaviour through an evolutionary process. Thus, intelligence and evolution are intimately connected. This has inspired evolutionary computation (EC) that simulates the evolutionary process to develop powerful techniques such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategies (ES) and evolutionary programming (EP) to solve real-world problems in learning, control, optimization and classification. GP discovers the relationship among data and expresses it as a LISP-S expression i.e., a computer program. Thus the goal of program discovery as a solution for a problem is addressed by GP in the framework of evolutionary computation. In this thesis, we address for the first time the problem of applying GP to mu1ticategory pattern classification. In supervised pattern classification, an input vector of m dimensions is mapped onto one of the n classes. It has a number of application areas such as remote sensing, medical diagnosis etc., A supervised classifier is developed by using a training set that contains representative samples of various classes present in the application. Supervised classification has been done earlier with maximum likelihood classifier: neural networks and fuzzy logic. The major considerations in applying GP to pattern classification are listed below: (i) GP-based techniques are data distribution-free i.e., no a priori knowledge is needed abut the statistical distribution of the data or no assumption such as normal distribution for data needs to be made as in MLC. (ii) GP can directly operate on the data in its original form. (iii) GP can detect the underlying but unknown relationship that mists among data and express it as a mathematical LISP S-expression. The generated LISP S-expressions can be directly used in the application environment. (iv) GP can either discover the most important discriminating features of a class during evolution or it requires minor post-processing of the LISP-S expression to discover the discriminant features. In a neural network, the knowledge learned by the neural network about the data distributions is embedded in the interconnection weights and it requires considerable amount of post-processing of the weights to understand the decision of the neural network. In 2-category pattern classification, a single GP expression is evolved as a discriminant function. The output of the GP expression can be +l for samples of one class and -1 for samples of the other class. When the GP paradigm is applied to an n-class problem, the following questions arise: Ql. As a typical GP expression returns a value (+l or -1) for a 2-class problem, how does one apply GP for the n-class pattern classification problem? Q2. What should be the fitness function during evolution of the GP expressions? Q3. How does the choice of a function set affect the performance of GP-based classification? Q4. How should training sets be created for evaluating fitness during the evolution of GP classifier expressions? Q5. How does one improve learning of the underlying data distributions in a GP framework? Q6. How should conflict resolution be handled before assigning a class to the input feature vector? Q7. How does GP compare with other classifiers for an n-class pattern classification problem? The research described here seeks to answer these questions. We show that GP can be applied to an n-category pattern classification problem by considering it as n 2-class problems. The suitability of this approach is demonstrated by considering a real-world problem based on remotely sensed satellite images and Fisher's Iris data set. In a 2-class problem, simple thresholding is sufficient for a discriminant function to divide the feature space into two regions. This means that one genetic programming classifier expression (GPCE) is sufficient to say whether or not the given input feature vector belongs to that class; i.e., the GP expression returns a value (+1 or -1). As the n-class problem is formulated as n 2-class problems, n GPCEs are evolved. Hence, n GPCE specific training sets are needed to evolve these n GPCEs. For the sake of illustration, consider a 5-class pat tern classification problem. Let n, be the number of samples that belong to class j, and N, be the number of samples that do not belong to class j, (j = 1,..., 5). Thus, N1=n2+n3+n4+n5 N2=n1+n3+n4+n5 N3=n1+n2+n4+n5 N4=n1+n2+n3+n5 N5=n1+n2+n3+n4 Thus, When the five class problem is formulated as five 2-class problems. we need five GPCEs as discriminant functions to resolve between n1 and N1, n2 and N2, n3 and N3, n4 and N4 and lastly n5 and N5. Each of these five 2-class problems is handled as a separate 2-class problem with simple thresholding. Thus, GPCE# l resolves between samples of class# l and the remaining n - 1 classes. A training set is needed to evaluate the fitness of GPCE during its evolution. If we directly create the training set, it leads to skewness (as n1 < N1). To overcome the skewness, an interleaved data format is proposed for the training set of a GPCE. For example, in the training set of GPCE# l, samples of class# l are placed alternately between samples of the remaining n - 1 classes. Thus, the interleaved data format is an artifact to create a balanced training set. Conventionally, all the samples of a training set are fed to evaluate the fitness of every member of the population in each generation. We call this "global" learning 3s GP tries to learn the entire training set at every stage of the evolution. We have introduced incremental learning to simplify the task of learning for the GP paradigm. A subset of the training set is fed and the size of the subset is gradually increased over time to cover the entire training data. The basic motivation for incremental learning is to improve learning during evolution as it is easier to learn a smaller task and then to progress from a smaller task to a bigger task. Experimental results are presented to show that the interleaved data format and incremental learning improve the performance of the GP classifier. We also show that the GPCEs evolved with an arithmetic function set are able to track variation in the input better than GPCEs evolved with function sets containing logical and nonlinear elements. Hence, we have used arithmetic function set, incremental learning, and interleaved data format to evolve GPCEs in our simulations. AS each GPCE is trained to recognize samples belonging to its own class and reject samples belonging to other classes a strength of association measure is associated with each GPCE to indicate the degree to which it can recognize samples belonging to its own class. The strength of association measures are used for assigning a class to an input feature vector. To reduce misclassification of samples, we also show how heuristic rules can be generated in the GP framework unlike in either MLC or the neural network classifier. We have also studied the scalability and generalizing ability of the GP classifier by varying the number of classes. We also analyse the performance of the GP classifier by considering the well-known Iris data set. We compare the performance of classification rules generated from the GP classifier with those generated from neural network classifier, (24.5 method and fuzzy classifier for the Iris data set. We show that the performance of GP is comparable to other classifiers for the Iris data set. We notice that the classification rules can be generated with very little post-processing and they are very similar to the rules generated from the neural network and C4.5 for the Iris data set. Incremental learning influences the number of generations available for GP to learn the data distribution of classes whose d is -1 in the interleaved data format. This is because the samples belonging to the true class (desired output d is +1) are alternately placed between samples belonging to other classes i.e., they are repeated to balance the training set in the interleaved data format. For example, in the evolution of GPCE for class# l, the fitness function can be fed initially with samples of class#:! and subsequently with the samples of class#3, class#4 and class#. So in the evaluation of the fitness function, the samples of class#kt5 will not be present when the samples of class#2 are present in the initial stages. However, in the later stages of evolution, when samples of class#5 are fed, the fitness function will utilize the samples of both class#2 and class#5. As learning in evolutionary computation is guided by the evaluation of the fitness function, GPCE# l gets lesser number of generations to learn how to reject data of class#5 as compared to the data of class#2. This is because the termination criterion (i.e., the maximum number of generations) is defined a priori. It is clear that there are (n-l)! Ways of ordering the samples of classes whose d is -1 in the interleaved data format. Hence a heuristic is presented to determine a possible order to feed data of different classes for the GPCEs evolved with incremental learning and interleaved data format. The heuristic computes an overlap index for each class based on its spatial spread and distribution of data in the region of overlap with respect to other classes in each feature. The heuristic determines the order in which classes whose desired output d is –1 should be placed in each GPCE-specific training set for the interleaved data format. This ensures that GP gets more number of generations to learn about the data distribution of a class with higher overlap index than a class with lower overlap index. The ability of the GP classifier to learn the data distributions depends upon the number of classes and the spatial spread of data. As the number of classes increases, the GP classifier finds it difficult to resolve between classes. So there is a need to partition the feature space and identify subspaces with reduced number of classes. The basic objective is to divide the feature space into subspaces and hence the data set that contains representative samples of n classes into subdata sets corresponding to the subspaces of the feature space, so that some of the subdata sets/spaces can have data belonging to only p classes (p < n). The GP classifier is then evolved independently for the subdata sets/spaces of the feature space. This results in localized learning as the GP classifier has to learn the data distribution in only a subspace of the feature space rather than in the entire feature space. By integrating the GP classifier with feature space partitioning (FSP), we improve classification accuracy due to localized learning. Although serial computers have increased steadily in their performance, the quest for parallel implementation of a given task has continued to be of interest in any computationally intensive task since parallel implementation leads to a faster execution than a serial implementation As fitness evaluation, selection strategy and population structures are used to evolve a solution in GP, there is scope for a parallel implementation of GP classifier. We have studied distributed GP and massively parallel GP for our approach to GP-based multicategory pattern classification. We present experimental results for distributed GP with Message Passing Interface on IBM SP2 to highlight the speedup that can be achieved over the serial implementation of GP. We also show how data parallelism can be used to further speed up fitness evaluation and hence the execution of the GP paradigm for multicategory pat tern classification. We conclude that GP can be applied to n-category pattern classification and its potential lies in its simplicity and scope for parallel implementation. The GP classifier developed in this thesis can be looked upon as an addition to the earlier statistical, neural and fuzzy approaches to multicategory pattern classification.

Nature has created complex biological structures that exhibit intelligent behaviour through an evolutionary process. Thus, intelligence and evolution are intimately connected. This has inspired evolutionary computation (EC) that simulates the evolutionary process to develop powerful techniques such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategies (ES) and evolutionary programming (EP) to solve real-world problems in learning, control, optimization and classification. GP discovers the relationship among data and expresses it as a LISP-S expression i.e., a computer program. Thus the goal of program discovery as a solution for a problem is addressed by GP in the framework of evolutionary computation. In this thesis, we address for the first time the problem of applying GP to mu1ticategory pattern classification. In supervised pattern classification, an input vector of m dimensions is mapped onto one of the n classes. It has a number of application areas such as remote sensing, medical diagnosis etc., A supervised classifier is developed by using a training set that contains representative samples of various classes present in the application. Supervised classification has been done earlier with maximum likelihood classifier: neural networks and fuzzy logic. The major considerations in applying GP to pattern classification are listed below: (i) GP-based techniques are data distribution-free i.e., no a priori knowledge is needed abut the statistical distribution of the data or no assumption such as normal distribution for data needs to be made as in MLC. (ii) GP can directly operate on the data in its original form. (iii) GP can detect the underlying but unknown relationship that mists among data and express it as a mathematical LISP S-expression. The generated LISP S-expressions can be directly used in the application environment. (iv) GP can either discover the most important discriminating features of a class during evolution or it requires minor post-processing of the LISP-S expression to discover the discriminant features. In a neural network, the knowledge learned by the neural network about the data distributions is embedded in the interconnection weights and it requires considerable amount of post-processing of the weights to understand the decision of the neural network. In 2-category pattern classification, a single GP expression is evolved as a discriminant function. The output of the GP expression can be +l for samples of one class and -1 for samples of the other class. When the GP paradigm is applied to an n-class problem, the following questions arise: Ql. As a typical GP expression returns a value (+l or -1) for a 2-class problem, how does one apply GP for the n-class pattern classification problem? Q2. What should be the fitness function during evolution of the GP expressions? Q3. How does the choice of a function set affect the performance of GP-based classification? Q4. How should training sets be created for evaluating fitness during the evolution of GP classifier expressions? Q5. How does one improve learning of the underlying data distributions in a GP framework? Q6. How should conflict resolution be handled before assigning a class to the input feature vector? Q7. How does GP compare with other classifiers for an n-class pattern classification problem? The research described here seeks to answer these questions. We show that GP can be applied to an n-category pattern classification problem by considering it as n 2-class problems. The suitability of this approach is demonstrated by considering a real-world problem based on remotely sensed satellite images and Fisher's Iris data set. In a 2-class problem, simple thresholding is sufficient for a discriminant function to divide the feature space into two regions. This means that one genetic programming classifier expression (GPCE) is sufficient to say whether or not the given input feature vector belongs to that class; i.e., the GP expression returns a value (+1 or -1). As the n-class problem is formulated as n 2-class problems, n GPCEs are evolved. Hence, n GPCE specific training sets are needed to evolve these n GPCEs. For the sake of illustration, consider a 5-class pat tern classification problem. Let n, be the number of samples that belong to class j, and N, be the number of samples that do not belong to class j, (j = 1,..., 5). Thus, N1=n2+n3+n4+n5 N2=n1+n3+n4+n5 N3=n1+n2+n4+n5 N4=n1+n2+n3+n5 N5=n1+n2+n3+n4 Thus, When the five class problem is formulated as five 2-class problems. we need five GPCEs as discriminant functions to resolve between n1 and N1, n2 and N2, n3 and N3, n4 and N4 and lastly n5 and N5. Each of these five 2-class problems is handled as a separate 2-class problem with simple thresholding. Thus, GPCE# l resolves between samples of class# l and the remaining n - 1 classes. A training set is needed to evaluate the fitness of GPCE during its evolution. If we directly create the training set, it leads to skewness (as n1 < N1). To overcome the skewness, an interleaved data format is proposed for the training set of a GPCE. For example, in the training set of GPCE# l, samples of class# l are placed alternately between samples of the remaining n - 1 classes. Thus, the interleaved data format is an artifact to create a balanced training set. Conventionally, all the samples of a training set are fed to evaluate the fitness of every member of the population in each generation. We call this "global" learning 3s GP tries to learn the entire training set at every stage of the evolution. We have introduced incremental learning to simplify the task of learning for the GP paradigm. A subset of the training set is fed and the size of the subset is gradually increased over time to cover the entire training data. The basic motivation for incremental learning is to improve learning during evolution as it is easier to learn a smaller task and then to progress from a smaller task to a bigger task. Experimental results are presented to show that the interleaved data format and incremental learning improve the performance of the GP classifier. We also show that the GPCEs evolved with an arithmetic function set are able to track variation in the input better than GPCEs evolved with function sets containing logical and nonlinear elements. Hence, we have used arithmetic function set, incremental learning, and interleaved data format to evolve GPCEs in our simulations. AS each GPCE is trained to recognize samples belonging to its own class and reject samples belonging to other classes a strength of association measure is associated with each GPCE to indicate the degree to which it can recognize samples belonging to its own class. The strength of association measures are used for assigning a class to an input feature vector. To reduce misclassification of samples, we also show how heuristic rules can be generated in the GP framework unlike in either MLC or the neural network classifier. We have also studied the scalability and generalizing ability of the GP classifier by varying the number of classes. We also analyse the performance of the GP classifier by considering the well-known Iris data set. We compare the performance of classification rules generated from the GP classifier with those generated from neural network classifier, (24.5 method and fuzzy classifier for the Iris data set. We show that the performance of GP is comparable to other classifiers for the Iris data set. We notice that the classification rules can be generated with very little post-processing and they are very similar to the rules generated from the neural network and C4.5 for the Iris data set. Incremental learning influences the number of generations available for GP to learn the data distribution of classes whose d is -1 in the interleaved data format. This is because the samples belonging to the true class (desired output d is +1) are alternately placed between samples belonging to other classes i.e., they are repeated to balance the training set in the interleaved data format. For example, in the evolution of GPCE for class# l, the fitness function can be fed initially with samples of class#:! and subsequently with the samples of class#3, class#4 and class#. So in the evaluation of the fitness function, the samples of class#kt5 will not be present when the samples of class#2 are present in the initial stages. However, in the later stages of evolution, when samples of class#5 are fed, the fitness function will utilize the samples of both class#2 and class#5. As learning in evolutionary computation is guided by the evaluation of the fitness function, GPCE# l gets lesser number of generations to learn how to reject data of class#5 as compared to the data of class#2. This is because the termination criterion (i.e., the maximum number of generations) is defined a priori. It is clear that there are (n-l)! Ways of ordering the samples of classes whose d is -1 in the interleaved data format. Hence a heuristic is presented to determine a possible order to feed data of different classes for the GPCEs evolved with incremental learning and interleaved data format. The heuristic computes an overlap index for each class based on its spatial spread and distribution of data in the region of overlap with respect to other classes in each feature. The heuristic determines the order in which classes whose desired output d is –1 should be placed in each GPCE-specific training set for the interleaved data format. This ensures that GP gets more number of generations to learn about the data distribution of a class with higher overlap index than a class with lower overlap index. The ability of the GP classifier to learn the data distributions depends upon the number of classes and the spatial spread of data. As the number of classes increases, the GP classifier finds it difficult to resolve between classes. So there is a need to partition the feature space and identify subspaces with reduced number of classes. The basic objective is to divide the feature space into subspaces and hence the data set that contains representative samples of n classes into subdata sets corresponding to the subspaces of the feature space, so that some of the subdata sets/spaces can have data belonging to only p classes (p < n). The GP classifier is then evolved independently for the subdata sets/spaces of the feature space. This results in localized learning as the GP classifier has to learn the data distribution in only a subspace of the feature space rather than in the entire feature space. By integrating the GP classifier with feature space partitioning (FSP), we improve classification accuracy due to localized learning. Although serial computers have increased steadily in their performance, the quest for parallel implementation of a given task has continued to be of interest in any computationally intensive task since parallel implementation leads to a faster execution than a serial implementation As fitness evaluation, selection strategy and population structures are used to evolve a solution in GP, there is scope for a parallel implementation of GP classifier. We have studied distributed GP and massively parallel GP for our approach to GP-based multicategory pattern classification. We present experimental results for distributed GP with Message Passing Interface on IBM SP2 to highlight the speedup that can be achieved over the serial implementation of GP. We also show how data parallelism can be used to further speed up fitness evaluation and hence the execution of the GP paradigm for multicategory pat tern classification. We conclude that GP can be applied to n-category pattern classification and its potential lies in its simplicity and scope for parallel implementation. The GP classifier developed in this thesis can be looked upon as an addition to the earlier statistical, neural and fuzzy approaches to multicategory pattern classification.

Parmi les diverses modalités biométriques qui permettent l'identification des personnes, l'iris est considéré comme très fiable, avec un taux d'erreur remarquablement faible. Toutefois, ce niveau élevé de performances est obtenu en contrôlant la qualité des images acquises et en imposant de fortes contraintes à la personne (être statique et à proximité de la caméra). Cependant, dans de nombreuses applications de sécurité comme les contrôles d'accès, ces contraintes ne sont plus adaptées. Les images résultantes souffrent alors de diverses dégradations (manque de résolution, artefacts...) qui affectent négativement les taux de reconnaissance. Pour contourner ce problème, il est possible d’exploiter la redondance de l’information découlant de la disponibilité de plusieurs images du même œil dans la séquence enregistrée. Cette thèse se concentre sur la façon de fusionner ces informations, afin d'améliorer les performances. Dans la littérature, diverses méthodes de fusion ont été proposées. Cependant, elles s’accordent sur le fait que la qualité des images utilisées dans la fusion est un facteur crucial pour sa réussite. Plusieurs facteurs de qualité doivent être pris en considération et différentes méthodes ont été proposées pour les quantifier. Ces mesures de qualité sont généralement combinées pour obtenir une valeur unique et globale. Cependant, il n'existe pas de méthode de combinaison universelle et des connaissances a priori doivent être utilisées, ce qui rend le problème non trivial. Pour faire face à ces limites, nous proposons une nouvelle manière de mesurer et d'intégrer des mesures de qualité dans un schéma de fusion d'images, basé sur une approche de super-résolution. Cette stratégie permet de remédier à deux problèmes courants en reconnaissance par l'iris: le manque de résolution et la présence d’artefacts dans les images d'iris. La première partie de la thèse consiste en l’élaboration d’une mesure de qualité pertinente pour quantifier la qualité d’image d’iris. Elle repose sur une mesure statistique locale de la texture de l’iris grâce à un modèle de mélange de Gaussienne. L'intérêt de notre mesure est 1) sa simplicité, 2) son calcul ne nécessite pas d'identifier a priori les types de dégradations, 3) son unicité, évitant ainsi l’estimation de plusieurs facteurs de qualité et un schéma de combinaison associé et 4) sa capacité à prendre en compte la qualité intrinsèque des images mais aussi, et surtout, les défauts liés à une mauvaise segmentation de la zone d’iris. Dans la deuxième partie de la thèse, nous proposons de nouvelles approches de fusion basées sur des mesures de qualité. Tout d’abord, notre métrique est utilisée comme une mesure de qualité globale de deux façons différentes: 1) comme outil de sélection pour détecter les meilleures images de la séquence et 2) comme facteur de pondération au niveau pixel dans le schéma de super-résolution pour donner plus d'importance aux images de bonnes qualités. Puis, profitant du caractère local de notre mesure de qualité, nous proposons un schéma de fusion original basé sur une pondération locale au niveau pixel, permettant ainsi de prendre en compte le fait que les dégradations peuvent varier d’une sous partie à une autre. Ainsi, les zones de bonne qualité contribueront davantage à la reconstruction de l'image fusionnée que les zones présentant des artéfacts. Par conséquent, l'image résultante sera de meilleure qualité et pourra donc permettre d'assurer de meilleures performances en reconnaissance. L'efficacité des approches proposées est démontrée sur plusieurs bases de données couramment utilisées: MBGC, Casia-Iris-Thousand et QFIRE à trois distances différentes. Nous étudions séparément l'amélioration apportée par la super-résolution, la qualité globale, puis locale dans le processus de fusion. Les résultats montrent une amélioration importante apportée par l'utilisation de la qualité globale, amélioration qui est encore augmentée en utilisant la qualité locale<br>Among the large number of biometric modalities, iris is considered as a very reliable biometrics with a remarkably low error rate. The excellent performance of iris recognition systems are obtained by controlling the quality of the captured images and by imposing certain constraints on users, such as standing at a close fixed distance from the camera. However, in many real-world applications such as control access and airport boarding these constraints are no longer suitable. In such non ideal conditions, the resulting iris images suffer from diverse degradations which have a negative impact on the recognition rate. One way to try to circumvent this bad situation is to use some redundancy arising from the availability of several images of the same eye in the recorded sequence. Therefore, this thesis focuses on how to fuse the information available in the sequence in order to improve the performance. In the literature, diverse schemes of fusion have been proposed. However, they agree on the fact that the quality of the used images in the fusion process is an important factor for its success in increasing the recognition rate. Therefore, researchers concentrated their efforts in the estimation of image quality to weight each image in the fusion process according to its quality. There are various iris quality factors to be considered and diverse methods have been proposed for quantifying these criteria. These quality measures are generally combined to one unique value: a global quality. However, there is no universal combination scheme to do so and some a priori knowledge has to be inserted, which is not a trivial task. To deal with these drawbacks, in this thesis we propose of a novel way of measuring and integrating quality measures in a super-resolution approach, aiming at improving the performance. This strategy can handle two types of issues for iris recognition: the lack of resolution and the presence of various artifacts in the captured iris images. The first part of the doctoral work consists in elaborating a relevant quality metric able to quantify locally the quality of the iris images. Our measure relies on a Gaussian Mixture Model estimation of clean iris texture distribution. The interest of our quality measure is 1) its simplicity, 2) its computation does not require identifying in advance the type of degradations that can occur in the iris image, 3) its uniqueness, avoiding thus the computation of several quality metrics and associated combination rule and 4) its ability to measure the intrinsic quality and to specially detect segmentation errors. In the second part of the thesis, we propose two novel quality-based fusion schemes. Firstly, we suggest using our quality metric as a global measure in the fusion process in two ways: as a selection tool for detecting the best images and as a weighting factor at the pixel-level in the super-resolution scheme. In the last case, the contribution of each image of the sequence in final fused image will only depend on its overall quality. Secondly, taking advantage of the localness of our quality measure, we propose an original fusion scheme based on a local weighting at the pixel-level, allowing us to take into account the fact that degradations can be different in diverse parts of the iris image. This means that regions free from occlusions will contribute more in the image reconstruction than regions with artefacts. Thus, the quality of the fused image will be optimized in order to improve the performance. The effectiveness of the proposed approaches is shown on several databases commonly used: MBGC, Casia-Iris-Thousand and QFIRE at three different distances: 5, 7 and 11 feet. We separately investigate the improvement brought by the super-resolution, the global quality and the local quality in the fusion process. In particular, the results show the important improvement brought by the use of the global quality, improvement that is even increased using the local quality

Técnicas biométricas procuraram identificar usuários pela textura da íris, impressão digital, traços faciais, entre outros. A íris humana apresenta características de textura que a classificam como uma peculiaridade biométrica de grande poder de discriminação no reconhecimento de pessoas. O objetivo deste trabalho é avaliar a eficiência de uma nova metodologia de análise de texturas em desenvolvimento no LAVI (Laboratório de Visão Computacional da EESC-USP) na identificação de indivíduos por meio da textura de sua íris. A metodologia denomina-se Local Fuzzy Pattern e tem sido utilizada com excelente desempenho com texturas gerais, naturais e artificiais. Este documento detalha as técnicas utilizadas para extração e normalização da textura da íris, a utilização e os resultados obtidos com o método Local Fuzzy Pattern aplicado à classificação biométrica da íris humana. Os resultados obtidos apresentam sensibilidade de até 99,7516% com a aplicação da metodologia proposta em bancos de imagens de íris humana disponíveis na internet demonstram a viabilidade da técnica proposta.<br>Biometric techniques sought to identify users by the texture of the iris, fingerprint, facial features, among others. The human iris have texture characteristics that rank it as a powerful biometric peculiarity on human recognition. The objective of this masters proposal is to investigate the efficiency of a new methodology of iris texture analysis currently in development in LAVI (Laboratório de Visão Computacional da EESC-USP). The methodology is called LFP (Local Fuzzy Pattern) and has been used with excellent overall performance on artificial and natural textures. This document details the techniques used for the extraction and normalization of the iris texture, the use and results of the local fuzzy pattern method applied to biometric classification of the human eye. The results show a sensibility of value up to 99.7516%, obtained by applying the proposed methodology on human iris photos from image database available on the internet does showing the viability of the technique.

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<br>Financiadora de Estudos e Projetos<br>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.<br>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.

Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.

The distribution of income differs in Ireland and Northern Ireland. Historically, Northern Ireland has been marked by lower levels of income and lower income inequality. The Gini coefficient, a widely used measure of income inequality which increases as income becomes more dispersed, has averaged 0.29 in Northern Ireland and slightly more than this, at 0.31 in Ireland, between 2003 and 2019. Using harmonised microsimulation models for Ireland (SWITCH) and Northern Ireland (UKMOD), we simulate the Gini coefficient to be 0.26 in Northern Ireland and 0.28 in Ireland in 2019, although these point estimates are not statistically different from each other. Nevertheless, as seemingly similar income distributions can come about for different underlying reasons, we analyse the drivers of income inequality in Ireland and Northern Ireland. Previous research has identified marked differences in demographics, working patterns, wage levels and the tax-benefit system between Ireland and Northern Ireland. These factors are important determinants of income distribution and are likely to contribute differently to how income is distributed in the two jurisdictions. Using a decomposition technique (following Bargain and Callan (2010); Doorley et al. (2021) and Sologon et al. (2021)), this research identifies the drivers of income inequality in Ireland and Northern Ireland in the year 2019. We isolate the relative contributions of market income differences - attributable to demographics, labour market participation and wage levels - and the tax-benefit system to differences in income distribution in the two jurisdictions. We find that differences in inequality in market, or pre-tax and transfer income, are driven by two counteracting forces. On the one hand, the younger and more highly educated population of Ireland results in relatively lower income inequality as there are relatively fewer people with no earnings. On the other hand, the higher and more unequal wages paid to workers in Ireland result in relatively higher market income inequality, all else equal. We estimate that differences in the tax-benefit system also influence the distribution of income in Ireland and Northern Ireland. The Irish tax system is more progressive and reduces income inequality more than the Northern Irish tax system. However, the level and coverage of means-tested benefits in Ireland is lower than that in Northern Ireland. Therefore the Irish means-tested benefit system is inequality-increasing compared to the Northern Irish means-tested benefit system. The combination of these two opposing effects results in similar overall levels of redistribution by the Irish and Northern Irish tax-benefit systems taken as a whole. This research sheds light on possible future developments in income inequality on the island of Ireland. Secular trends in population aging and upskilling are likely to affect the distribution of pre-tax and transfer income in both Ireland and Northern Ireland. The latter is likely to be particularly important in Northern Ireland, where baseline levels of education are lower. This research also finds that, if there is any convergence in the future between the tax-benefit systems of Ireland and Northern Ireland, in the context of increased economic co-operation on the island of Ireland, there may be a limited impact on income inequality due to opposing forces in the tax and benefit system. An understanding of these forces and their impact on income inequality - in isolation and together - can help to guide any such move towards future co-operation.

Rapid detection of pathogens and hazardous elements in fresh fruits and vegetables after harvest requires the use of advanced sensor technology at each step in the farm-to-consumer or farm-to-processing sequence. Fourier-transform infrared (FTIR) spectroscopy and the complementary Raman spectroscopy, an advanced optical technique based on light scattering will be investigated for rapid and on-site assessment of produce safety. Paving the way toward the development of this innovative methodology, specific original objectives were to (1) identify and distinguish different serotypes of Escherichia coli, Listeria monocytogenes, Salmonella typhimurium, and Bacillus cereus by FTIR and Raman spectroscopy, (2) develop spectroscopic fingerprint patterns and detection methodology for fungi such as Aspergillus, Rhizopus, Fusarium, and Penicillium (3) to validate a universal spectroscopic procedure to detect foodborne pathogens and non-pathogens in food systems. The original objectives proposed were very ambitious hence modifications were necessary to fit with the funding. Elaborate experiments were conducted for sensitivity, additionally, testing a wide range of pathogens (more than selected list proposed) was also necessary to demonstrate the robustness of the instruments, most crucially, algorithms for differentiating a specific organism of interest in mixed cultures was conceptualized and validated, and finally neural network and chemometric models were tested on a variety of applications. Food systems tested were apple juice and buffer systems. Pathogens tested include Enterococcus faecium, Salmonella enteritidis, Salmonella typhimurium, Bacillus cereus, Yersinia enterocolitis, Shigella boydii, Staphylococus aureus, Serratiamarcescens, Pseudomonas vulgaris, Vibrio cholerae, Hafniaalvei, Enterobacter cloacae, Enterobacter aerogenes, E. coli (O103, O55, O121, O30 and O26), Aspergillus niger (NRRL 326) and Fusarium verticilliodes (NRRL 13586), Saccharomyces cerevisiae (ATCC 24859), Lactobacillus casei (ATCC 11443), Erwinia carotovora pv. carotovora and Clavibacter michiganense. Sensitivity of the FTIR detection was 103CFU/ml and a clear differentiation was obtained between the different organisms both at the species as well as at the strain level for the tested pathogens. A very crucial step in the direction of analyzing mixed cultures was taken. The vector based algorithm was able to identify a target pathogen of interest in a mixture of up to three organisms. Efforts will be made to extend this to 10-12 key pathogens. The experience gained was very helpful in laying the foundations for extracting the true fingerprint of a specific pathogen irrespective of the background substrate. This is very crucial especially when experimenting with solid samples as well as complex food matrices. Spectroscopic techniques, especially FTIR and Raman methods are being pursued by agencies such as DARPA and Department of Defense to combat homeland security. Through the BARD US-3296-02 feasibility grant, the foundations for detection, sample handling, and the needed algorithms and models were developed. Successive efforts will be made in transferring the methodology to fruit surfaces and to other complex food matrices which can be accomplished with creative sampling methods and experimentation. Even a marginal success in this direction will result in a very significant breakthrough because FTIR and Raman methods, in spite of their limitations are still one of most rapid and nondestructive methods available. Continued interest and efforts in improving the components as well as the refinement of the procedures is bound to result in a significant breakthrough in sensor technology for food safety and biosecurity.

Migrant integration allows migrants to contribute to the economic, social, cultural and political life of the country they migrate to; it is also important for social cohesion and inclusive growth. By examining how migrants fare relative to the majority population in key life domains, this report seeks to illustrate the challenges, successes and opportunities for migrant integration in Ireland today. Monitoring integration can provide crucial information for policy and public conversations around migration, and the profile of migrants in Ireland. Ireland’s migration context has changed considerably in recent years: there have been relatively high numbers of arrivals from Ukraine to Ireland, and a marked increase in the number of people seeking international protection since the last monitoring report on integration. A rise in the cost of living and an acute shortage of housing continue to be significant challenges for all living in Ireland, including many migrants. This report is the ninth in a series of monitoring reports on integration that began in 2011. It considers how migrants – generally defined as those born outside the State – fare relative to the Irish-born population across four key life domains: employment, education, social inclusion and active citizenship. The report also provides updates on migration and integration policy. Integration indicators are based on high-quality, nationally representative survey data for the latest available time points, supplemented with administrative statistics on forced migrants, where possible. Key headline figures are presented in Table A: individual chapters disaggregate migrant groups by region of origin. Chapter 1 presents recent trends in migration and provides a profile of the migrant population in Ireland. The rise in immigration since the end of the COVID-19 pandemic continued into 2024, nearly reaching its 2007 peak. The 2007 peak was dominated by migration from within the European Union (EU): recent patterns indicate a shift towards non-EU migration, for work, study and international protection. In 2023, 22% of the population were born outside the State: just over half of this subgroup was born in the United Kingdom (UK – including Northern Ireland) or the EU, while the remainder was born across a diverse range of non-EU countries. This is an increase from 20% born outside the State in 2021.

The Hindu Kush Himalaya (HKH) region is highly susceptible to the influence of monsoon, a periodic wind system, especially in the Indian Ocean and southern Asia. The summer monsoon, between June and September, is the major source of precipitation in the region with significant impacts on the hydrology of its rivers, which form the lifeline of nearly two billion people in the region. While a good monsoon is essential for replenishing these river systems, malevolence of water-related disasters such as floods, landslides, storms, heat waves, wildfires, droughts, glacial lake outburst floods (GLOFs), is becoming more pronounced in this region under the exacerbating effects of climate change. For instance, in the last forty years or so more than 70% of the flood events in the region took place during the summer monsoon season. Against this backdrop, the HKH Monsoon Outlook 2025 serves as a preliminary frame of reference into the summer monsoon conditions likely to prevail in the region during June – September 2025, based on seasonal forecasts for South Asia at large. The seasonal estimates are collated from the APEC1 Climate Centre (APCC), Copernicus Climate Service (C3S), International Research Institute for Climate and Society (IRI), 31st Session of South Asian Climate Outlook Forum (SASCOF -31) and several national agencies for meteorological assessments. With the forecasters unanimously predicting oceanic and atmospheric phenomena that usually affect (read disrupt) monsoon patterns in South Asia – such as, the El Nino Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Madden-Jullian Oscillation (MJO) activities – to be neutral and /or weak during JuneJuly-August 2025, the likelihood of summer monsoon precipitations is potent this year. However, based on the incidence of below-normal snow cover in the Northern Hemisphere, especially between January and March 2025, along with an estimated mean summer temperature anomaly in South Asia ranging from 0.5°C to 2°C above normal, they also predict high probability of above-normal precipitations for most of South Asia, including HKH swathes. Looking at this possibility, we surmise that the HKH region is likely to be exposed to intensifying risks of rain-induced hazards like flash floods, landslides, and GLOFs if precipitations are intense or prolonged.