IRIS RECOGNITION FAILURE IN BIOMETRICS: A REVIEW

More than twenty years iris has been claimed to be the most stable modality in human lifetime. However, the iris recognition produces ‘failure to match’ problem which made the known is unknown user or the genuine is recognized as imposter in the biometric systems. Apparently, failure to recognize the real user as in the database is due to a few assumptions: aging of the sensor, changes in how a person uses the system such as the threshold settings and template aging effect. This paper focuses on template aging effect since it is on ongoing problem faced in iris recognition. Many studies attempted several techniques to overcome the problem in every phase which consists of three general phases: the pre-processing, feature extraction and feature matching. Therefore, the purpose of this paper is to study and identify the problems in iris recognition that lead to failure-to-match in biometrics

Распознавание человека по изображению радужной оболочки глаза: проблемы и достижения

В статье рассматривается современное состояние актуального раздела биометрики – распознавания человека по изображению радужной оболочки глаза. Анализируются возникающие проблемы и подходы к их решению. Представлены результаты экспериментальных исследований распознавания личности по изображению радужной оболочки глаза.У статті розглядається сучасний стан актуального розділу біометрики – розпізнавання людини за райдужною оболонкою ока. Аналізуються проблеми, що виникають, і підходи до їх рішення. Представлені результати експериментальних досліджень розпізнавання особи за зображенням райдужної оболонки ока.The current state of the actual branch of biometrics, i.e. human identification by iris image analysis, is discussed in the paper. We analyze existing problems and variants of their solution. Some experimental results on iris recognition are presented

Iris Recognition in Multiple Spectral Bands: From Visible to Short Wave Infrared

The human iris is traditionally imaged in Near Infrared (NIR) wavelengths (700nm-900nm) for iris recognition. The absorption co-efficient of color inducing pigment in iris, called Melanin, decreases after 700nm thus minimizing its effect when iris is imaged at wavelengths greater than 700nm. This thesis provides an overview and explores the efficacy of iris recognition at different wavelength bands ranging from visible spectrum (450nm-700nm) to NIR (700nm-900nm) and Short Wave Infrared (900nm-1600nm). Different matching methods are investigated at different wavelength bands to facilitate cross-spectral iris recognition.;The iris recognition analysis in visible wavelengths provides a baseline performance when iris is captured using common digital cameras. A novel blob-based matching algorithm is proposed to match RGB (visible spectrum) iris images. This technique generates a match score based on the similarity between blob like structures in the iris images. The matching performance of the blob based matching method is compared against that of classical \u27Iris Code\u27 matching method, SIFT-based matching method and simple correlation matching, and results indicate that the blob-based matching method performs reasonably well. Additional experiments on the datasets show that the iris images can be matched with higher confidence for light colored irides than dark colored irides in the visible spectrum.;As part of the analysis in the NIR spectrum, iris images captured in visible spectrum are matched against those captured in the NIR spectrum. Experimental results on the WVU multispectral dataset show promise in achieving a good recognition performance when the images are captured using the same sensor under the same illumination conditions and at the same resolution. A new proprietary \u27FaceIris\u27 dataset is used to investigate the ability to match iris images from a high resolution face image in visible spectrum against an iris image acquired in NIR spectrum. Matching in \u27FaceIris\u27 dataset presents a scenario where the two images to be matched are obtained by different sensors at different wavelengths, at different ambient illumination and at different resolution. Cross-spectral matching on the \u27FaceIris\u27 dataset presented a challenge to achieve good performance. Also, the effect of the choice of the radial and angular parameters of the normalized iris image on matching performance is presented. The experiments on WVU multispectral dataset resulted in good separation between genuine and impostor score distributions for cross-spectral matching which indicates that iris images in obtained in visible spectrum can be successfully matched against NIR iris images using \u27IrisCode\u27 method.;Iris is also analyzed in the Short Wave Infrared (SWIR) spectrum to study the feasibility of performing iris recognition at these wavelengths. An image acquisition setup was designed to capture the iris at 100nm interval spectral bands ranging from 950nm to 1650nm. Iris images are analyzed at these wavelengths and various observations regarding the brightness, contrast and textural content are discussed. Cross-spectral and intra-spectral matching was carried out on the samples collected from 25 subjects. Experimental results on this small dataset show the possibility of performing iris recognition in 950nm-1350nm wavelength range. Fusion of match scores from intra-spectral matching at different wavelength bands is shown to improve matching performance in the SWIR domain

A Longitudinal Analysis on the Feasibility of Iris Recognition Performance for Infants 0-2 Years Old

The focus of this study was to longitudinally evaluate iris recognition for infants between the ages of 0 to 2 years old. Image quality metrics of infant and adult irises acquired on the same iris camera were compared. Matching performance was evaluated for four groups, infants 0 to 6 months, 7 to 12 months, 13 to 24 months, and adults. A mixed linear regression model was used to determine if infants’ genuine similarity scores changed over time. This study found that image quality metrics were different between infants and adults but in the older group, (13 to 24 months old) the image quality metric scores were more likely to be similar to adults. Infants 0 to 6 months old had worse performance at an FMR of 0.01% than infants 7 to 12 months, 13 to 24 months, and adults

Information Processing for Biological Signals: Application to Laser Doppler Vibrometry

Signals associated with biological activity in the human body can be of great value in clinical and security applications. Since direct measurements of critical biological activity are often difficult to acquire noninvasively, many biological signals are measured from the surface of the skin. This simplifies the signal acquisition, but complicates post processing tasks. Modeling these signals using the underlying physics may not be accurate due to the inherent complexities of the human body. The appropriate use of such models depends on the application of interest. Models developed in this dissertation are motivated by underlying physiology and physics, and are capable of expressing a wide range of signal variability without explicitly invoking physical quantities. An approach for the processing of biological signals is developed using graphical models. Graphical models describe conditional dependence between random variables on a graph. When the graph is a tree, efficient algorithms exist to compute sum-marginals or max-marginals of the joint distribution. Some of the variables correspond to the measured signal, while others may represent the hidden internal dynamics that generate the observed data. Three levels of hidden dynamics are outlined, which enable models to be constructed that track internal dynamics on differing time scales. Expectation maximization algorithms are used to compute parameter estimates. Experimental results of this approach are presented for a novel method of recording bio-mechanical activity using a Laser Doppler Vibrometer. The LDV measures surface velocity on the basis of the Doppler shift. This device is targeted on the neck overlying the carotid artery, and the proximity of the carotid to the skin results in a strong signal. Vibrations and movements from within the carotid are transmitted to the surface of the skin, where they are sensed by the LDV. Changes in the size of the carotid due to variations in blood pressure are sensed at the skin surface. In addition, breathing activity may be inferred from the LDV signal. Individualized models are evaluated systematically on LDV data sets that were acquired under resting conditions on multiple occasions. Model fit is evaluated both within and across recording sessions. Model parameters are interpreted in terms of the underlying physiology. Pressure wave physics in a series of elastic tubes is presented to explore the underlying physics of blood flow in the carotid. Mechanical movements of the carotid walls are related to the underlying pressure, and therefore the cardiovascular activity of the heart and vasculature. This analysis motivates a model that can be estimated from experimental data. Resulting models are interpreted for the LDV signal. The graphical models are applied to the problem of identity verification using the LDV signal. Identity verification is an important problem in which the claimed identity is either accepted or rejected by an automated system. The system design that is used is based on a loglikelihood ratio test using models that are trained during an enrollment phase. A score is computed and compared to a threshold. Performance is given in the form of False Nonmatch and False Match empirical error rates as a function of the threshold. Confidence intervals are computed that take into account correlations between the system decisions

Recognition of Nonideal Iris Images Using Shape Guided Approach and Game Theory

Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The main objective of this thesis is to develop a nonideal iris recognition system by using active contour methods, Genetic Algorithms (GAs), shape guided model, Adaptive Asymmetrical Support Vector Machines (AASVMs) and Game Theory (GT). In this thesis, the proposed iris recognition method is divided into two phases: (1) cooperative iris recognition, and (2) noncooperative iris recognition. While most state-of-the-art iris recognition algorithms have focused on the preprocessing of iris images, recently, important new directions have been identified in iris biometrics research. These include optimal feature selection and iris pattern classification. In the first phase, we propose an iris recognition scheme based on GAs and asymmetrical SVMs. Instead of using the whole iris region, we elicit the iris information between the collarette and the pupil boundary to suppress the effects of eyelid and eyelash occlusions and to minimize the matching error. In the second phase, we process the nonideal iris images that are captured in unconstrained situations and those affected by several nonideal factors. The proposed noncooperative iris recognition method is further divided into three approaches. In the first approach of the second phase, we apply active contour-based curve evolution approaches to segment the inner/outer boundaries accurately from the nonideal iris images. The proposed active contour-based approaches show a reasonable performance when the iris/sclera boundary is separated by a blurred boundary. In the second approach, we describe a new iris segmentation scheme using GT to elicit iris/pupil boundary from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan's algorithm to form a unified approach, which is robust to noise and poor localization and less affected by weak iris/sclera boundary. Finally, to further improve the segmentation performance, we propose a variational model to localize the iris region belonging to the given shape space using active contour method, a geometric shape prior and the Mumford-Shah functional. The verification and identification performance of the proposed scheme is validated using four challenging nonideal iris datasets, namely, the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and the WVU Nonideal, plus the non-homogeneous combined dataset. We have conducted several sets of experiments and finally, the proposed approach has achieved a Genuine Accept Rate (GAR) of 97.34% on the combined dataset at the fixed False Accept Rate (FAR) of 0.001% with an Equal Error Rate (EER) of 0.81%. The highest Correct Recognition Rate (CRR) obtained by the proposed iris recognition system is 97.39%

Robust ECG based person identification system

Identity theft is a burgeoning issue. Gaining unauthorized access to computer network tends to compromise the system which could potentially cause undetected fatal destruction and disastrous consequences for individuals and the nation. It is to the extent of taking down communication networks, paralyzing transportation systems and crippling power grids. If security system are burdensome, people may avoid using them, preferring functionality and convenience. For these reasons, an effective security mechanism needs to be deployed in combating identity crimes. Therefore, this thesis proposes of implementing biometric technology as a viable solution for the aforementioned problems. In the recent years, the electrocardiogram (ECG) signal was introduced as a potential biometric modality to overcome issues of currently available biometric attributes which could be falsified by gummy fingerprints, static iris and face images, voice mimics and fake signatures. When a person is having a heartbeat, automatically it proclaims that the person exist and is alive. Thus, the advantage as a life indicator mechanism verifies the presence of a person during the time of recognition. For the past decade, preliminary investigations on the validity of using ECG based biometric have been manifested with different person recognition methods to support its usability in security and privacy applications. Even though, ECG based biometric has set its ground in recognizing people, however, the underlying issues that governs a practical biometric system have not been properly addressed. Basic problems which require further attention are fundamental issues which touch the aspects of reliability and robustness of an ECG based biometric system in a real life scenario. Thus, in this thesis, we have identified four main research problems which are essentially important to increase user acceptability of ECG based biometric recognition covering different aspects of a practical biometric system such as distinctiveness, permanence, collectability and performance. The research issues being posed in this thesis are the selection of extracted biometric features, subject recognition with different pathological and physiological conditions, performing biometric with low sampling frequency signals and applying ECG based biometric in mobile surroundings. This thesis suggests of solving ECG based biometric recognition raised problems in a holistic perspective which does not limit the implementations to certain groups of users but looking at the issue as a whole and in a boarder avenue so that it could be applicable to almost all walks of life. A single optimum biometric system that supersedes others does not exist as each biometric modality is based on the nature of the implementation and application. Nevertheless, ECG based biometric features give a strong indication that it would be well accepted by users in the future due to the automatic liveness detection factor which is available in every human being that further expands to people with disabilities such as amputees and those who are visually impaired. Therefore, this thesis is substantial and vital as to assist and provide alternative person identification mechanism to present security and privacy applications in the quest to combat identity crimes