A fusion approach to unconstrained iris recognition

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.

This research introduces a noise-resistant and computational efficient segmentation approach towards less constrained iris recognition. The UBIRIS.v2 database which contains close-up eye images taken under visible light is used to test the proposed algorithm. The proposed segmentation approach is based on a modified and fast Hough transform augmented with a newly developed strategy to define iris boundaries with multi-arcs and multi-lines. This optimized iris segmentation approach achieves excellent results in both accuracy (2% error) and execution speed (≤0.5s / image) using a 2.4GHz Intel® Q6600 processor with 2GB of RAM. This 2% error is an Exclusive-OR function in term of disagreeing pixels between the correct iris considered by the NICE.I committee and the segmented results from the proposed approach. The segmentation performance was independently evaluated in the “Noisy Iris Challenge Evaluation”, involving 97 participants worldwide, and ranking this research group in the top 6.

Iris recognition has been tested to the most accurate biometrics using high resolution near infrared images. However, it does not work well under visible wavelength illumination. Sclera recognition, however, has been shown to achieve reasonable recognition accuracy under visible wavelengths. Combining iris and sclera recognition together can achieve better recognition accuracy. However, image quality can significantly affect the recognition accuracy. Moreover, in unconstrained situations, the acquired eye images may not be frontally facing. In this research, we proposed a feature quality-based multimodal unconstrained eye recognition method that combine the respective strengths of iris recognition and sclera recognition for human identification and can work with frontal and off-angle eye images. The research results show that the proposed method is very promising.

The texture of the iris is commonly represented as an iris code in iris recognition systems. While several approaches have been presented for generating iris codes, relatively few comparison techniques have been proposed. In this paper, we take advantage of the availability of several frames from an iris video to create a single optimized iris code. This is achieved by performing both row-wise and column-wise optimization of iris codes. Inconsistent bits are accurately detected and masked in the final iris code. Our experiments demonstrate that by exploiting variations within the comparison scores of different rows and columns of N frames, we are able to derive the number of consistent bits in the final iris code thereby resulting in significant improvement in recognition performance. We compare our algorithm with well-known methods, namely, Fragile bit masking, Signal fusion and, two Score Fusion techniques. Experimental results on a dataset of 986 iris videos show that the proposed method is encouraging and comparable to the best algorithms in the current literature. To our knowledge, this is the first work that makes use of the best rows and columns from different frames in an iris video to improve performance.

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

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.

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.

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.

Cross-spectral iris recognition represents the ability of the system to identify the iris images acquired in different electromagnetic spectrums. An iris captured in the near-infrared spectrum (NIR) is matched with an iris obtained in the visual light spectrum (VIS) to boost the recognition performance. In cross-spectral iris recognition, the illumination factor between NIR and VIS images significantly degrades the recognition performance. Therefore, the existing method only achieved recognition performance with an equal error rate (EER) larger than 5%, and it is a challenging issue for cross-spectral performance to have EER below 5%. In this paper, we improve iris recognition performance by concatenating the Gradientfaces-based normalization technique (GRF) to a standard (conventional) iris recognition method to alleviate the illumination effect. In addition, we integrate the GRF with a Gabor filter, a difference of Gaussian (DoG) filter, and texture descriptors, namely a binary statistical image feature (BSIF) and a local binary pattern (LBP). The experimental results show that the GRF can boost the cross-spectral iris recognition performance with an EER equals to 1.69%. In addition, the best cross-spectral iris recognition performance is achieved when the GRF is integrated with the Gabor filter and the BSIF.

The importance of personal authentication is gradually increasing with the development of the information society. Biometrics identification technology plays an important role in cyberspace. Unlike other biometrics such as the face or fingerprints, iris recognition has high reliability for personal identification. Iris recognition methods are classified into four categories: the phase-based method (Daugman, 1993), the zero-crossing representation-based method (Boles and Boashash, 1998; Sanchez-Avila and Sanchez-Reillo, 2005), the texture-based method (Wildes, 1997; Ma et al., 2003), and local intensity variation (Ma et al., 2004, a;b). Using the internal CASIA dataset (CBSR, 2005), Ma et al. evaluates the proposed algorithm by comparing the performance of other iris recognition methods proposed by Daugman, Wildes, and Boles and Boashash (Ma et al., 2004, b). The experimental results show the equal error rates (EER) of respective algorithms (Ma, Daugman, Wildes, and Boles) are 0.07%, 0.08%, 1.76%, and 8.13%, respectively. In other studies, the Daugman’s method which is a representative algorithm of iris recognition is also evaluated using the subset of internal CASIA dataset (Sun et al., 2006) and the CASIA iris image 1.0 database (Wang et al., 2007), which is available from the CASIA web site. The EERs of Daugman’s method reported in Sun et al. and Wang et al. are 0.70% and 0.67%, respectively. These analysis indicate the high accuracy of recognition performance although the EERs of Daugman’s method described in these papers are not the same because the iris segmentation method including eyelid and eyelash detection would not be exactly the same. Iris recognition technology are applied in various fields. Especially, the iris recognition algorithm embedded on a mobile phone requires robustness to rotation changes because capturing the iris pattern by a hand using a camera built in the mobile phone causes the rotation changes. However, the iris recognition methods described above are generally fragile in rotation variation. We previously proposed a rotation spreading neural network (R-SAN net) that focused on spatial recognition/memory systems (parietal cortex(PG)) in the brain and recognized an object’s orientation and shape (Nakamura et al., 1998; Yoshikawa and Nakamura, 2000). The R-SAN net can simultaneously recognize the orientation of the object irrespective of its shape, and the shape irrespective of its orientation. The characteristics of the R-SAN net are to use a two-dimensional input pattern in a polar coordinate system converted from the Cartesian coordinate system. The R-SAN net is suitable for the shape and orientation recognition of concentric circular patterns. The orientation recognition performance of R-SAN net allows the 10

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.

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.

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 Iris recognition is commonly used as a security system due to its robustness against imposters. Iris datasets are huge and hence those datasets occupy more space. Iris image compression has become an important part of better performance like speed and data storage. The portable Iris system is in huge demand. That portable systems need to transmit the iris images through a very small bandwidth channel. To reduce the time for transferring a huge number of data over small bandwidth channel, iris file can be compressed to some extent to minimize the size. Another problem is that when an image is captured, it captures some noise that disturbs the recognition performance, so, denoising is required for noise-free images. This paper separately analyzes the impact of wavelet compression along with denoising on iris images. The compression analysis is done using Embedded Zero Tree Wavelet, the other technique used is Set Partitioning in Hierarchical Tree and the third technique used is Spatial-Orientation Tree Wavelet. Denoising is done using different wavelets Daubechies, Haar, Biorthogonal and Fejer-Korovkin. The impact of the wavelet compression and denoising techniques on recognition performance are compared with False Rejection Rate and False Acceptance Rate. The quality of a compressed image is calculated with different quality metrics. This work establishes that compression and denoising of the images minimally affect the recognition performance.