A Design Paradigm for Long Range Iris Recognition Systems with Sparsity Based Techniques for Iridal Texture Enhancement

Iris Recognition for Personal Identification System

Biometric technology deals with recognizing the uniqueness of individuals based on their exclusive Physical or behavioral characteristics. The developments in science & technology have made it possible to use biometrics in application when it is required to establish (or) confirm the identity of persons. Amongst a range of method, Iris recognition is a hastily intensifying technique of biometric technology. A large range of identification methods is used either verify or decide the identity of person request their processes. This Paper provides the inspiration for image acquisition, segmentation, normalization standed on the human being Iris imaging. A combination method is used to fragment the eye's digital image. The mined Iris region was then standardized by using Image registration method. A phase correlation method is installed for this Iris image registration principle. Investigational consequences demonstrate that our system is reasonably efficient and gives hopeful performance.

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.

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.

Wavefront coding technology can extend the depth of field of the iris imaging system, but the iris image obtained through the system is coded and blurred and can't be used for the recognition algorithm directly. The paper presents a fuzzy iris image restoration method used in the wavefront coding system. After the restoration, the images can be used for the following processing. Firstly, the wavefront coded imaging system is simulated and the optical parameter is analyzed, through the simulation we can get the system's point spread function (PSF). Secondly, using the blurred iris image and PSF to do a blind restoration to estimate a appropriate PSFe. Finally, based on the return value PSFe of PSF, applying the regularization filter on the blurred image. Experimental results show that the proposed method is simple and has fast processing speed. Compared with the traditional restoration algorithms of Wiener filtering and Lucy-Richardson filtering, the recovery image that got through the regularization filtering is the most similar with the original iris image.

Despite numerous biometric challenges promoting iris and ocular recognition from less controlled images, iris localization and segmentation are still challenging topics. Examples include the Multiple Biometrics Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), Noisy Iris Challenge Evaluation (NICE) and Iris Challenge Evaluation (ICE). For controlled environments and as long as iris segmentation algorithms can be tuned for the employed set, the segmentation problem can be managed. However, independent studies have shown, that algorithms may fail considerably if preconditions are not met. For datasets like FOCS segmentation performance ranging from 51% accuracy (using an integro-differential operator), to 90% (for active contours) is reported. Violations of preconditions or assumptions of controlled conditions such as particular hardware challenge the generalisation of successful segmentation techniques. Especially non-circularity of boundaries, non-frontal acquisition, or blurred images can impose difficulties. On the other hand, very flexible parameterless solutions allowing high degrees of freedom (e.g. off-axis acquisition) are sensitive to noise or reflections. They sometimes deliver inferior results to classical techniques for images where certain preconditions hold. Examples of challenging iris images include weak boundaries or reflections in visible-range iris images, making it harder for iris local- ization and segmentation algorithms to properly segment acquired images. Further undesirable conditions are on-the-move motion blur, out-of-focus images, narrowed eyes to a slit, or images with weak contrast. Besides accuracy, also speed and usability play important factors for efficient iris segmentation. For quick rejection of frames in video streams containing no iris image or unlikely leading to a successful verification, quality measures as predictors of recognition or segmentation accuracy can be used. This is useful to save precious processing time, as processing errors at early stages usually also result in classification errors. This chapter reviews recent approaches of iris segmentation techniques and discusses approaches towards robust segmentation, highlighting the following topics: • Segmentation performance assessment: Performance assessments are usually done with regards to recognition accuracy (thus evaluating an entire processing chain rather than individual algorithms). However, systematic seg- mentation errors can have a minor impact on performance if the same algorithm is used to segment gallery and probe images. This scenario is not realistic in cross-sensor applications. However, ground-truth only evaluations are restrictive, as they do not account for the tolerance of small segmentation in- accuracies by the employed feature extraction and comparison algorithm (e.g. via pooling operations). Different forms of segmentation accuracy evaluation are presented. There are separate segmentation-only challenges (e.g. NICE) and also ground-truth sets available, to which this chapter con- tributes a manual segmentation of the multispectral iris database UTIRIS. • Near infrared (NIR) vs. visible range (VIS) iris segmentation and the tuning problem: Algorithms developed for specific image datasets or character- istics are not agnostic towards the type of imagery used. NIR images, for example, exhibit a pronounced inner pupillary boundary, while VIS images tend to have better outer limbic contrast. This is usually exploited by seg- mentation algorithms to achieve better accuracy on particular datasets. While more generic techniques offering the same processing chain for VIS and NIR images have been suggested, tuning can have a strong impact on perfor- mance. This chapter presents modifications to the open source USIT - Univer- sity of Salzburg Iris Toolkit for better performance, if prior knowledge on iris data characteristics (NIR vs. VIS, iris-to-pupil ratio, size of iris, etc.) is available. Source code of the modifications is released. • Iris fusion at segmentation-level: Instead of developing completely new seg- mentation algorithms, the idea behind segmentation-level fusion is to make efficient use of different existing optimised approaches estimating their ac- curacy. The best individual segmentation technique can be used as the final segmentation result. Techniques can even be combined at a lower level leading to new segmentation results. For example, over- and under-segmentation methods can be fused to balance errors towards more robust iris segmentation. A learning-based approach predicting ground-truth segmentation performance is compared to non-quality based combinations in a multispectral setup assessing the impact of NIR and VIS spectras on performance. New techniques are analysed with regards to a challenging multispectral NIR and VIS iris database involving the same subjects. This allows a direct comparison of robustness of segmentation results (localisation of pupillary and limbic boundaries) across the two spectra and illustrates the high versatility of the proposed methods.

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.

Most commercial iris recognition systems use infrared or near-infrared cameras to capture the iris images because they show rich and complex textures that can be used to identify a person. This paper presents the design and implementation of a multispectral iris acquisition system that can efficiently capture and store iris images exposed under different wavelengths of illumination. The goal is enable the study of wavelength effects on iris recognition performance. A set of multispectral illuminators is designed to sequentially activate for short periods of time to expose the iris to spectral bands in the 405 nm – 1550 nm range. A specialized camera is used to capture iris images for each wavelength. A software program was developed to control the illuminators, camera, and the image acquisition process.

A new method which improves the laser drilling efficiency and allows for drilling at large laser-target standoff distances has been developed. A dual pulse laser was found to be capable of penetrating stainless steel targets with a thickness greater than 1/4” at standoff distances of 1 meter. The standoff distance is defined by the distance between the focusing lens and the target. The dual pulse laser produced two free-running laser pulses from a single Nd:glass or Nd:YAG laser head driven by integrated power supplies. The output consisted of the combination of a high energy, long duration pulse (pulse 1) with a high peak power laser pulse (pulse 2) fired at the end of pulse 1. Pulse 1 is used to produce a large molten pool of liquid which is expelled by the vapor recoil pressure generated by the interaction of the high intensity pulse 2 with the metal.A new method which improves the laser drilling efficiency and allows for drilling at large laser-target standoff distances has been developed. A dual pulse laser was found to be capable of penetrating stainless steel targets with a thickness greater than 1/4” at standoff distances of 1 meter. The standoff distance is defined by the distance between the focusing lens and the target. The dual pulse laser produced two free-running laser pulses from a single Nd:glass or Nd:YAG laser head driven by integrated power supplies. The output consisted of the combination of a high energy, long duration pulse (pulse 1) with a high peak power laser pulse (pulse 2) fired at the end of pulse 1. Pulse 1 is used to produce a large molten pool of liquid which is expelled by the vapor recoil pressure generated by the interaction of the high intensity pulse 2 with the metal.

This paper investigates the effect of segmentation errors on off-angle iris recognition. We first segment the inner and outer boundaries of off-angle iris images by fitting the best ellipses to boundaries. Then, we validate the segmentation results using Ground Truth Tool and fix the possible errors. Second, we add random errors on iris segmentation parameters to examine how Hamming distance distribution changes for different amount of segmentation error. In order to add errors, we first group the parameters into three sets as ellipse center (i.e., x, y), minor and major axis (i.e., r 1 , r 2 ) and orientation (θ) and changed the parameters by adding positive and negative random error as a noise to the ground truth segmentation parameters. Our purpose is to show how performance of iris recognition is affected if there is an error in segmentation and how important is to have a very robust segmentation algorithm for off-angle iris recognition. We test the effect of segmentation errors using our off-angle iris dataset that contains images from -50° to +50° in gaze angles. Based on our experimental results, we observed that error in ellipse center decrease the recognition performance worse than minor and major axis and orientation. The main reason is the normalization process tolerates the small amount of error in minor and major axis and bit shift in matching handles the small variations in the orientation errors. However, if the error in the ellipse orientation exceeds the limit of the bit shift method tolerance, the performance of the off-angle iris recognition dramatically drops.

This report describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looks at recognition from high-resolution still images and three-dimensional (3D) face images, and measures performance for still images taken under controlled and uncontrolled illumination. The ICE 2006 reports iris recognition performance from left and right iris images. The FRVT 2006 results from controlled still images and 3D images document an order-of-magnitude improvement in recognition performance over the FRVT 2002. This order-of-magnitude improvement was one of the goals of the preceding technology development effort, the Face Recognition Grand Challenge (FRGC). The FRVT 2006 and the ICE 2006 compared recognition performance from very-high resolution still face images, 3D face images, and single-iris images. On the FRVT 2006 and the ICE 2006 datasets, recognition performance was comparable for all three biometrics. In an experiment comparing human and algorithm performance, the best-performing face recognition algorithms were more accurate than humans. These and other results are discussed in detail.

This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images.

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.

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.