Optimization of Iris Codes for Improved Recognition

This dissertation focuses on iris biometrics. Although the iris is the most accurate biometric, its adoption has been relatively slow. Conventional iris recognition systems utilize still eye images captured in ideal environments and require highly constrained subject presentation. A drop in recognition performance is observed when these constraints are removed as the quality of the data acquired is affected by heterogeneous factors. For iris recognition to be widely adopted, it can therefore be argued that the image capture must be facilitated and better performance should be achieved in less constrained imaging conditions. The research work presented in this dissertation demonstrates how performance in iris recognition systems is improved by adopting a video-based approach. The following components have been investigated in this study and presented in relevant publications: (1) Robust eye extraction method of eye images in face videos captured at a distance and on the move (2) Selection of optimal frames in iris videos (3) Iris segmentation in less constrained environments (4) An automated method for predicting inaccurate iris segmentation (5) Optimization of iris codes for improved recognition. The main results and novelties of this work include: Firstly, the development of a fast and accurate method for detecting eye images in face videos. Secondly, this work demonstrates that selection of optimal frames in NIR iris videos lead to better recognition performance. Thirdly, an accurate and robust iris segmentation model for eye images captured in uncontrolled conditions is proposed. Fourthly, this research presents a fully automated segmentation evaluation model for detection of in- correctly segmented iris images. Finally, a new method for optimization of several iris codes into a single highly optimized iris code is introduced. Our results and experiments suggest that incorporation of the above methods in traditional iris recognition systems will be useful for the adoption of this technology by a larger community.

Among biometrics, iris and ocular recognition systems are the methods that recognize eye features in an image. Such iris and ocular regions must have a certain image resolution to achieve a high recognition performance; otherwise, the risk of performance degradation arises. This is even more critical in the case of iris recognition where detailed patterns are used. In cases where such low-resolution images are acquired and the acquisition apparatus and environment cannot be improved, recognition performance can be enhanced by obtaining high-resolution images with methods such as super-resolution reconstruction. However, previous survey papers have mainly summarized studies on high-resolution iris and ocular recognition, but do not provide detailed summaries of studies on low-resolution iris and ocular recognition. Therefore, we investigated high-resolution iris and ocular recognition methods and introduced in detail the low-resolution iris and ocular recognition methods and methods of solving the low-resolution problem. Furthermore, since existing survey papers have focused on and summarized studies on traditional handcrafted feature-based iris and ocular recognition, this survey paper also introduced the latest deep learning-based methods in detail.

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology.

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of seven quality factors: defocus blur, motion blur, off-angle, occlusion, specular reflection, lighting, and pixel-counts on the performance of traditional iris recognition system. We have concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most. We further designed a fully automated iris image quality evaluation block that operates in two steps. First each factor is estimated individually, then the second step involves fusing the estimated factors by using Dempster-Shafer theory approach to evidential reasoning. The designed block is tested on two datasets, CASIA 1.0 and a dataset collected at WVU. Considerable improvement in recognition performance is demonstrated when removing poor quality images evaluated by our quality metric. The upper bound on processing complexity required to evaluate quality of a single image is O(<i>n</i>² log <i>n</i>), that of a 2D-Fast Fourier Transform.

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.

The iris is a stable biometric trait that has been widely used for human recognition in various applications. However, deployment of iris recognition in forensic applications has not been reported. A primary reason is the lack of human-friendly techniques for iris comparison. To further promote the use of iris recognition in forensics, the similarity between irises should be made visualizable and interpretable. Recently, a human-in-the-loop iris recognition system was developed, based on detecting and matching iris crypts. Building on this framework, we propose a new approach for detecting and matching iris crypts automatically. Our detection method is able to capture iris crypts of various sizes. Our matching scheme is designed to handle potential topological changes in the detection of the same crypt in different images. Our approach outperforms the known visible-feature-based iris recognition method on three different data sets. In particular, our approach achieves over 22% higher rank one hit rate in identification, and over 51% lower equal error rate in verification. In addition, the benefit of our approach on multi-enrollment is experimentally demonstrated.

The gaining popularity of the visible spectrum iris recognition has sparked the interest in adopting it for various access control applications. Along with the popularity of visible spectrum iris recognition comes the threat of identity spoofing, presentation, or direct attack. This paper presents a novel scheme for detecting video presentation attacks in visible spectrum iris recognition system by magnifying the phase information in the eye region of the subject. The proposed scheme employs modified Eulerian video magnification (EVM) to enhance the subtle phase information in eye region and novel decision module to classify it as artefact(spoof attack) or normal presentation. The proposed decision module is based on estimating the change of phase information obtained from EVM, specially tailored to detect presentation attacks on video-based iris recognition systems in visible spectrum. The proposed scheme is extensively evaluated on the newly constructed database consisting of 62 unique iris video acquired using two smartphones-iPhone 5S and Nokia Lumia 1020. We also construct the artefact database with 62 iris acquired by replaying normal presentation iris video on iPad with retina display. Extensive evaluation of proposed presentation attack detection (PAD) scheme on the newly constructed database has shown an outstanding performance of average classification error rate = 0% supporting the robustness of the proposed PAD scheme.

— Iris recognition system provides an approach for individual identification and is regarded as the sophisticated biometric identification system. Therefore, the exclusive features of iris patterns should be extracted and processed. In this paper, a new feature extraction method according to ridgelet transform for identifying the iris images is provided. At first, after segmentation and normalization the collarette area of iris images has been extracted. Then we improve the quality of image by using median filter, histogram equalization, and the two-dimensional (2-D) Wiener filter as well. Finally, ridgelet transform is employed for extracting features and then, the binary bit stream vector is generated. The Hamming distance (HD) between the input bit stream vector and stored vectors is calculated for iris identification. The experimental results show efficiency of the proposed method. Keywords- Iris identification, ridgelet transform. I. I NTRODUCTION Biometrics is the technology of identification a person from a physical characteristic and it is based on behavioral characteristics of human structures. Among existing different structural features, it was shown that exclusive features like iris pattern have very suitable features [1]. Some properties of the human iris that enhance its suitability for automatic identification include: 1) its inherent isolation and protection from the external environment, being an internal organ of the eye, behind the cornea and the aqueous humor 2) the impossibility of surgically modifying it without high risk of damaging the user’s vision 3) its physiological response to light, which provides the detection of a dead or plastic iris and 4) never are there two irises alike, even for identical twin. Many researchers developed iris recognition systems. Daugman [2] proposed an algorithm which was based on iris codes. Integral differential operators are used to detect the centre and diameter of the iris. The image is converted from cartesian to polar transform and rectangular representation of the region of interest is generated. Feature extraction algorithm uses the complex valued 2-D Gabor wavelets to generate the iris codes which are then matched using HDs. Boles and Boashash [3] used zero crossing point of 1-D wavelet at different levels on same center circle of the iris and pupil centralism. Wildes [4] used Laplace pyramid with four various resolution levels to range and normalize correlation for comparison between input image and database images. Ma et al. [5] used 4 dimensional Haar wavelet analysis in four steps decomposition. Krichen et al. [6] used wavelet packets to produce an iris code at each

Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.

Iris is claimed to be one of the best biometrics. We have collected a large data set of iris images, intentionally sampling a range of quality broader than that used by current commercial iris recognition systems. We have re-implemented the Daugman-like iris recognition algorithm developed by Masek. We have also developed and implemented an improved iris segmentation and eyelid detection stage of the algorithm, and experimentally verified the improvement in recognition performance using the collected dataset. Compared to Masek's original segmentation approach, our improved segmentation algorithm leads to an increase of over 6% in the rank-one recognition rate.

Personal identification based on biometrics technology is a trend in the future. Traditional approaches, for example, keys, ID cards, username and password, are neither satisfactory nor reliable enough in many security fields, biometrics authorizations based on face, iris, fingerprint have become a hot research filed. In those methods, iris recognition is regarded as a high accuracy verification technology, so that many countries have the same idea of adopting iris recognition to improve the safety of their key departments. The human iris can also be considered a valid biometrics for personal identification [Richard P W, 1996]. Biometrics recognition based on iris patterns is a hotspot as face recognition and fingerprint recognition recently years. The iris is the colored ring on the human eye between the pupil and the white sclera. Lots of physical biometric can be found in the colored ring of tissue that surrounds the pupil, such as corona, crypts, filaments, flecks, pits, radial furrows and striations. The iris features can be encoded by mathematical representation so that the patterns can be compared easily. In real-time iris recognition application system, iris localization is a very important step for iris recognition. The iris regions segmentation accuracy and localization real-time performance will affect the whole recognition system’s correct rate and effectiveness for large-scale database. Because iris region is a small object and has low grey value, it is very difficult to capture high contrast iris image clearly. In order to improve iris image contrast, usually some illuminations such as near infrared light source are used to increase intensity; however these illuminations may result in some faculas in iris image and affect iris segmentation and iris features. Here, we will discuss iris recognition system’s algorithm, all steps of iris recognition system will be introduced in details. Finally, we will show the experimental results based on iris database.

Iris Recognition (IR) across illumination variations is a problem of fundamental importance in computer vision. In this paper, we propose two novel techniques, viz., Triangular DCT based Feature Extraction (T-DCT) and Radon Transform based Pre-processing, to improve the performance of an IR system. T-DCT is used for efficient feature extraction and Radon Transform is used for curve detection in iris images. A Binary Particle Swarm Optimization (BPSO) based feature selection algorithm is used to search the feature space for optimal feature subset. Individual stages of the IR system are examined and an attempt is made to improve each stage. Experimental results obtained by applying the proposed algorithm on three benchmark iris databases, namely Phoenix, MMU and IITD dataset, show that the proposed system out performs other IR systems. A significant increase in the recognition rate and a substantial reduction in the number of selected features are observed.

Security is an important issue in biometric recognition systems. In recent years, many researchers proposed to use watermarking to improve the security of biometric systems, but some people concern whether the embedded watermarks will influence recognition results. In this paper, we investigate the effects of several fragile and semi-fragile watermarking methods on the iris recognition performance. Experimental results demonstrate that, even images are fully embedded, fragile watermarking methods nearly have no effects on the recognition performance, while semi-fragile watermarking methods which embed watermark in the visually important components of images have larger effects on the recognition performance than the semi-fragile watermarking methods that embed watermark in the visually unimportant components of images. And embedding parameters, such as embedding strength and watermark length, also have some influences on the recognition results.

Iris is an evenly highlighted radial membrane with complex patterns that are perceptible upon near inspection, which exists behind the cornea of the eye with a changeable circular opening called the pupil. Iris Recognition System is a technique of identifying people using those complex distinctive features in patterns. Generally, Iris recognition system is used in security allied applications such as, authenticating PCs, network and mobile devices, physical and logical premises access control, national border control, secure banking and financial transactions, national identity like AADHAAR in India etc. This paper reviews the state-of-art design and implementation of various iris recognition systems. The contributions of the paper include: 1) conferring the importance, applications and deployment of iris recognition system related to human identification; 2) providing an analysis on iris recognition methods in effect; 3, discussing the present research defies; 4) providing commendations for the future research on iris recognition system.

In state-of-the-art iris recognition systems, the input iris image has to be compared with a large number of templates in database. When the scale of iris database increases, they are much less efficient and accurate. In this paper, we propose a novel iris classification method to attack this problem in iris recognition systems. Firstly, we learned a small finite dictionary of visual words(clusters in the feature space), which are called Iris-Textons, to represent visual primitives of iris images. Then the Iris-Texton histograms are used to represent the global features of iris textures. Finally, K-means algorithm is used for classifying iris images into five categories. Based on the proposed method, the correct classification rate is 95% in a five-category iris database. By combining this method with traditional iris recognition algorithm, our system shows better performance in terms of both speed and accuracy.KeywordsGlobal FeatureIris ImageIris RecognitionVisual DictionaryIris DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.