Improving Iris Recognition Accuracy Via Cascaded Classifiers

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 'Iris Code' 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 'FaceIris' 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 'FaceIris' 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 'FaceIris' 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 'IrisCode' method.;Iris is also analyzed in the Short

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

The use of iris as biometric trait has emerged as one of the most preferred method because of its uniqueness, lifetime stability and regular shape. Moreover it shows public acceptance and new user-friendly capture devices are developed and used in a broadened range of applications. Currently, iris recognition systems work well with frontal iris images from cooperative users. Nonideal iris images are still a challenge for iris recognition and can significantly affect the accuracy of iris recognition systems. Moreover, accurate localization of different eye’s parts from videos or still images is a crucial step in many image processing applications that range from iris recognition in Biometrics to gaze estimation for Human Computer Interaction (HCI), impaired people aid or, even, marketing analysis for products attractiveness. Notwithstanding this, actually, most of available implementations for eye’s parts segmentation are quite invasive, imposing a set of constraints both on the environment and on the user itself limiting their applicability to high security Biometrics or to cumbersome interfaces. In the first part of this Chapter, we propose a novel approach to segment the sclera, the white part of the eye. We concentrate on this area since, thanks to the dissimilarity with other eye’s parts, its identification can be performed in a robust way against light variations, reflections and glasses lens flare. An accurate sclera segmentation is a fundamental step in iris and pupil localization, even in non-frontal noisy images. Furthermore its particular geometry can be fruitfully used for accurate eyeball rotation estimation. The proposed technique is based on a statistical approach (supported by some heuristic assumptions) to extract discriminating descriptors for sclera and non-sclera pixels. A Support Vector Machine (SVM) is then used as a final supervised classifier. Once the eyeball rotation angle respect to the camera optical axis is estimated and the limbus (the boundary between the iris and the sclera) is extracted, we propose a method to correct off-angle iris image. Taking into account the eye morphology and the reflectance properties of the external transparent layers, we can evaluate the distorting effects that are present in the acquired image. The correction algorithm proposed includes a first modeling phase of the human eye and a simulation phase where the acquisition geometry is reproduced and the distortions are evaluated. Finally we obtain an image which does not contain the distorting effects due to jumps in the refractive index. We show how this correction process reduces the intra-class variations for off-angle iris images.

The use of iris as biometric trait has emerged as one of the most preferred method because of its uniqueness, lifetime stability and regular shape. Moreover it shows public acceptance and new user-friendly capture devices are developed and used in a broadened range of applications. Currently, iris recognition systems work well with frontal iris images from cooperative users. Nonideal iris images are still a challenge for iris recognition and can significantly affect the accuracy of iris recognition systems. In this paper, we propose a method to correct off-angle iris image. Taking into account the eye morphology and the reflectance properties of the external transparent layers, we can evaluate the distorting effect that is present in the acquired image. The correction algorithm proposed includes a first modeling phase of the human eye, a segmentation of the acquired image, and a simulation phase where the acquisition geometry is reproduced and the distortions are evaluated. Finally we obtain an image which does not contain the distorting effects due to jumps in the refractive index. We show how this correction process reduce the intra-class variations for off-angle iris images.

The traditional iris recognition systems require equal high quality human iris images. A cheap image acquisition system has difficulty in capturing equal high quality iris images. This paper describes a new feature representation method for iris recognition robust to noises. The disc-shaped iris image is first convolved with a low pass filter along the radial direction. Then, the radially smoothed iris image is decomposed in the angular direction using a one-dimensional continuous wavelet transform. Each decomposed one-dimensional waveform is approximated by an optimal piecewise linear curve connecting a small set of node points. The set of node points is used as a feature vector. The optimal approximation procedure reduces the feature vector size while maintaining recognition accuracy. The similarity between two iris images is measured by the normalized cross-correlation coefficients between optimal curves. The similarity between two iris images is estimated using mid-frequency bands. The rotation of one-dimensional signals due to the head tilt is estimated using the lowest frequency component. Experimentally we show the proposed method produces superb performance in iris recognition.

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.

During the image acquisition procedure of an automatic iris recognition system, the iris image with low quality may lead to the personal identification failure in some cases. Therefore it is very important to adopt the image quality evaluation procedure before the image processing. In this paper, we proposed a fast image quality evaluation method based on weighted information entropy combining iris image segmentation through localization. Through this method, we can fast grade the images and pick out the high quality iris images from the video sequence captured by the image acquisition device. Experimental results show that this method can quickly and effectively screen out appropriate images to meet the requirements of the iris recognition algorithm. It can also improve the speed and accuracy of the iris recognition system.

The role of biometrics, as an integral component of security infrastructures, has been firmly established in today's world. Its ability to accurately identify people from a distance has served as an important life-saving tool in the arsenalof our men and women in uniform, out in war theaters; it has also served as an effective forensics tool for our police force and off-late, is being increasingly adopted to secure sensitive transactions such as those with financial institutions.Traditionally, fingerprint patterns and facial photographs have held sway over identity veri fication tools; the former, because of its uniqueness and the latter because of its ubiquity. However, over the past decade, iris patterns have alsocome to the fore because of the combined advantage of uniqueness across the populace as well as the ability to image these patterns from a distance. Today most commercial iris acquisition systems report recognition accuracies upwards of 90%, over entire populations. However, the caveat is that these systems areconstrained to operate very close to the user, owing to the challenges involved in achieving sufficient resolution over the iride texture from large distances. This dissertation aims to push the boundaries of iris imaging devices and in-troduces theoretical and practical considerations in designing a novel long range iris recognition system. The aim is to design a system capable of acquiring enrollment quality iris images, similar in quality to those captured by short range systems such as IriShield, LG, PIER, HIIDE systems, at much larger stand-off distances. The challenges involved in acquiring images with sufficient spatialresolution, when subjects are too far out from the optimal acquisition range, are explored and potential solutions are described. First, we describe the design methodology behind creating a long range iris system, to aid the reader inunderstanding the key design considerations when using available commercial o -the-shelf hardware. Competing objectives such as achieving high magnifi cation while maintaining sufficient depth of field, using a sensor with large pixel pitch for low noise performance while resolving the required patterns, etc. are discussed. As a result of this work, the rst long range iris acquisition system of its kind, capable of acquiring enrollment iris images from up to 9mand matching up to 12m, has been designed and built. We show the system, present lessons learned during the design process as well as empirical results for the imaging performance. The latter half of the dissertation develops aniris texture enhancement algorithm for restoring degraded iris images - specifically noisy, low-resolution and out-of-focus/blurred images, which are often encountered in the real world. The method is developed and evaluated usingsynthesized degraded images from the NIST Iris Challenge Evaluation (ICE) database. A signifi cant improvement in verification performance is also seen, when using this enhancement method, on actual degraded images captured with the proposed long range iris system. The proposed enhancement method is modeled as a linear regression problem that exploits the sparsity of iris textures inan over-complete dictionary. Improvements in iris recognition performance using the enhanced textures are studied in the latter half of this dissertation. In summary, this work enhances and exceeds the capabilities of current iris imaging systems for the explicit purpose of accurately identifying threats using iride patterns, from distances larger than are currently possible. This will serve as anecessary tool in the arsenal of security establishments to identify threats, save lives and to protect our freedoms for generations to come.

Although iris recognition technology has been reported to be more stable and reliable than other biometric systems, performance can be degraded due to many factors such as small eyes, camera defocusing, eyelash occlusions and specular reflections on the surface of glasses. In this paper, we propose a new multi-unit iris authentication method that uses score level fusion based on a support vector machine (SVM) and a quality assessment method for mobile phones. Compared to previous research, this paper presents the following two contributions. First, we reduced the false rejection rate and improved iris recognition accuracy by using iris quality assessment. Second, if even two iris images were determined to be of bad quality, we captured the iris images again without using a recognition process. If only one iris image among the left and right irises was regarded as a good one, it was used for recognition. However, if both the left and right iris images were good, we performed multi-unit iris recognition using score level fusion based on a SVM. Experimental results showed that the accuracy of the proposed method was superior to previous methods that used only one good iris image or those methods that used conventional fusion methods.

Iris recognition has emerged as one of the most accurate and convenient biometric for person identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris recognition accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This article presents such a periocular-assisted dynamic framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation, e.g., improvement in the recognition accuracy of 22.9%, 10.4% and 14.6% on three databases under both the verification and recognition scenarios.

Iris Recognition for Personal Identification System

In order to improve the performance of iris recognition, a novel method for iris recognition based on block theory and self-adaptive feature selection is proposed in this paper. Firstly, the normalized iris image is decomposed by convolving with multi-scale and multi-orientation Gabor filters, and then separated into several blocks, the block feature vector which includes mean and variance of Gabor coefficients inside each block can be obtained through statistical techniques, the iris feature vector of the whole iris image is then constructed by conjugating the block feature vector in row column order, finally the two-classifier of iris image are established based on the most distinguishable features, and the multi-classifiers of iris image are established by voting mechanism, and the performance is test by CASIA iris database. The results show that, compared with the traditional iris recognition methods, the proposed method has improved the iris recognition rate.

Iris recognition is a means of biometric identification. A key part of the recognition system using iris is the extraction of prominent texture information or features in the iris. The identification delay in iris recognition can be reduced by reducing the feature vector generated from the feature extraction of iris images. A new form of local binary pattern (LBP) termed LBPX is proposed in this paper as an iris feature extraction method. For this, input eye images are processed and converted to normalized iris images employing circular Hough transformation and Daugman’s rubber sheet model. Next, LBPX is applied to the normalized images. In this LBPX stage, rotation-invariant LBP operation takes place. The performance of LBPX based recognition system adopting iris image is evaluated in terms of accuracy and feature vector length. This is done for three datasets CASIA-IRIS-V4, UBIRIS and IITD. Results indicate that LBPX can achieve acceptable accuracy values of 97.20%, 96% and 96.40% for CASIA-IRIS-V4, UBIRIS and IITD datasets, respectively. Furthermore, results show LBPX outperforms existing feature extraction methods in terms of reduced feature-length, ensuring faster iris recognition.

In this thesis we propose a set of algorithms to reduce the false rejection rate of iris recognition. Even though high recognition accuracy is claimed for iris recognition algorithms, high false rejection rates cause the impediment in worldwide use of iris biometrics.;A novel iris segmentation algorithm for non-ideal iris images treating iris as an elliptical object is proposed. Further, quality of the extracted iris image is improved using SVM based enhancement algorithm. In this algorithm, selected enhancement algorithms globally enhance the iris image and the learning algorithm synergistically fuses local information from these intermediate enhanced images. 1D log polar Gabor wavelet is then used to extract the textural features from the enhanced iris image and Euler numbers are used to extract the topological features. The extracted textural features give a global description of the iris image whereas the topological features are rotation, translation and scaling invariant. These two features are fused using the proposed match score and decision fusion algorithms. Among the three proposed fusion algorithm, SVM learning based match score fusion algorithm outperforms other fusion algorithms. Using CASIA, Miles, UBIRIS and UPOL iris databases, experimental results show that the proposed algorithm gives reduced failure to enroll rate with comparable accuracy.

Iridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as reasonable and logical guidelines for the detection and identification of disorders. Therefore, after scanning the image of iris advance study of disorder can be carried out for detecting the condition of organ. Initially by the service of an adaptive histogram, the image of eye should be separated from part of the image captured. Next the images of iris are classified and recognized using machine learning algorithm Support Vector machine or Support Vector Networks. The features are extracted from images of iris using white Gaussian filters which are then used as a feature descriptor. These descriptors count the occurrences of gradient orientation and magnitude in localized portions of an image. Then convert the image of iris to a gray scaled image, final image is standardized. Next is to convert it into rectangular shape and then assembling the HMM images of eyes related to the kidney. The final level is to diagnose the edge of image of iris HMM. By analysing end results, condition of the organ can be diagnosed and results can be obtained from the iris recognition system.