Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Comparative Study of Different Window Sizes Setting in Median Filter for Off-angle Iris Recognition

Iris recognition is one of the most popular biometric recognition that has increased in the number of acceptance user gradually because of the reliability and accuracy provided by this system. However, this accuracy is highly correlated with the quality of iris image captured. Thus, a poor quality of the image captured required an enhancement technique. This study aims to identify the optimum window size for the median filter. Identifying the optimum window size setting required template matching value result of the off-angle iris recognition. The lowest value obtained showed that the window size applied was optimized. The result of this study demonstrated, for WVU-OA dataset for 15 degrees off-angle iris of right and left eyes, the window size of [5 5] and [7 7] respectively are optimum to maximize the median filter function. Meanwhile, for 30 degrees off-angle iris of right and left eyes data, the optimum windows size proposed are [7 7] and [5 5] respectively. On the other hand, analysis using UBIRIS dataset showed that the optimum window size for 30 degrees off-angle iris, both right and left eye is [7 7] which is able to maximize the performance of the median filter. In conclusion, the effective value to be applied to all dataset are [5 5] and [7 7] because in most cases it provides a better template matching compared to without applying the filtering method

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%

A framework for biometric recognition using non-ideal iris and face

Off-angle iris images are often captured in a non-cooperative environment. The distortion of the iris or pupil can decrease the segmentation quality as well as the data extracted thereafter. Moreover, iris with an off-angle of more than 30° can have non-recoverable features since the boundary cannot be properly localized. This usually becomes a factor of limited discriminant ability of the biometric features. Limitations also come from the noisy data arisen due to image burst, background error, or inappropriate camera pixel noise. To address the issues above, the aim of this study is to develop a framework which: (1) to improve the non-circular boundary localization, (2) to overcome the lost features, and (3) to detect and minimize the error caused by noisy data. Non-circular boundary issue is addressed through a combination of geometric calibration and direct least square ellipse that can geometrically restore, adjust, and scale up the distortion of circular shape to ellipse fitting. Further improvement comes in the form of an extraction method that combines Haar Wavelet and Neural Network to transform the iris features into wavelet coefficient representative of the relevant iris data. The non-recoverable features problem is resolved by proposing Weighted Score Level Fusion which integrates face and iris biometrics. This enhancement is done to give extra distinctive information to increase authentication accuracy rate. As for the noisy data issues, a modified Reed Solomon codes with error correction capability is proposed to decrease intra-class variations by eliminating the differences between enrollment and verification templates. The key contribution of this research is a new unified framework for high performance multimodal biometric recognition system. The framework has been tested with WVU, UBIRIS v.2, UTMIFM, ORL datasets, and achieved more than 99.8% accuracy compared to other existing methods

Comparative study of different window sizes setting in median filter for off-angle iris recognition

Iris recognition is one of the most popular biometric recognition that has increased in the number of acceptance user gradually because of the reliability and accuracy provided by this system. However, this accuracy is highly correlated with the quality of iris image captured. Thus, a poor quality of the image captured required an enhancement technique. This study aims to identify the optimum window size for the median filter. Identifying the optimum window size setting required template matching value result of the off-angle iris recognition. The lowest value obtained showed that the window size applied was optimized. The result of this study demonstrated, for WVU-OA dataset for 15 degrees off-angle iris of right and left eyes, the window size of [5 5] and [7 7] respectively are optimum to maximize the median filter function. Meanwhile, for 30 degrees off-angle iris of right and left eyes data, the optimum windows size proposed are [7 7] and [5 5] respectively. On the other hand, analysis using UBIRIS dataset showed that the optimum window size for 30 degrees off-angle iris, both right and left eye is [7 7] which is able to maximize the performance of the median filter. In conclusion, the effective value to be applied to all dataset are [5 5] and [7 7] because in most cases it provides a better template matching compared to without applying the filtering method

Novel techniques in iris recognition

Using Daugman\u27s algorithm and comparable alternatives, we find that we are able to identify an iris with as little as less than half of the iris information available, and an equal error rate comparable with that of popular biometrics like the fingerprint and face recognition biometrics. Different experiments were done based on percentage of iris shown, the resolution of the iris, and the position of the iris covered to determine if partial iris recognition is a viable biometric. It was found after over 500,000 different iris comparisons amongst five different experiments that regardless of the model used and the resolution, the equal error rates of partial iris recognition were competitive with its more popular counterparts. There is a slight decrease in the equal error rate in partial iris recognition, but not nearly as drastic as expected

Improving Iris Recognition through Quality and Interoperability Metrics

The ability to identify individuals based on their iris is known as iris recognition. Over the past decade iris recognition has garnered much attention because of its strong performance in comparison with other mainstream biometrics such as fingerprint and face recognition. Performance of iris recognition systems is driven by application scenario requirements. Standoff distance, subject cooperation, underlying optics, and illumination are a few examples of these requirements which dictate the nature of images an iris recognition system has to process. Traditional iris recognition systems, dubbed stop and stare , operate under highly constrained conditions. This ensures that the captured image is of sufficient quality so that the success of subsequent processing stages, segmentation, encoding, and matching are not compromised. When acquisition constraints are relaxed, such as for surveillance or iris on the move, the fidelity of subsequent processing steps lessens.;In this dissertation we propose a multi-faceted framework for mitigating the difficulties associated with non-ideal iris. We develop and investigate a comprehensive iris image quality metric that is predictive of iris matching performance. The metric is composed of photometric measures such as defocus, motion blur, and illumination, but also contains domain specific measures such as occlusion, and gaze angle. These measures are then combined through a fusion rule based on Dempster-Shafer theory. Related to iris segmentation, which is arguably one of the most important tasks in iris recognition, we develop metrics which are used to evaluate the precision of the pupil and iris boundaries. Furthermore, we illustrate three methods which take advantage of the proposed segmentation metrics for rectifying incorrect segmentation boundaries. Finally, we look at the issue of iris image interoperability and demonstrate that techniques from the field of hardware fingerprinting can be utilized to improve iris matching performance when images captured from distinct sensors are involved

Adaptive noise reduction and code matching for IRIS pattern recognition system

Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system

An Investigation of Iris Recognition in Unconstrained Environments

Iris biometrics is widely regarded as a reliable and accurate method for personal identification and the continuing advancements in the field have resulted in the technology being widely adopted in recent years and implemented in many different scenarios. Current typical iris biometric deployments, while generally expected to perform well, require a considerable level of co-operation from the system user. Specifically, the physical positioning of the human eye in relation to the iris capture device is a critical factor, which can substantially affect the performance of the overall iris biometric system. The work reported in this study will explore some of the important issues relating to the capture and identification of iris images at varying positions with respect to the capture device, and in particular presents an investigation into the analysis of iris images captured when the gaze angle of a subject is not aligned with the axis of the camera lens. A reliable method of acquiring off-angle iris images will be implemented, together with a study of a database thereby compiled of such images captured methodically. A detailed analysis of these so-called “off-angle” characteristics will be presented, making possible the implementation of new methods whereby significant enhancement of system performance can be achieved. The research carried out in this study suggests that implementing carefully new training methodologies to improve the classification performance can compensate effectively for the problem of off-angle iris images. The research also suggests that acquiring off-angle iris samples during the enrolment process for an iris biometric system and the implementation of the developed training configurations provides an increase in classification performance