Iris Indexing Techniques: A Review
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paper_title: Image understanding for iris biometrics : A survey

paper_content:

This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. We also suggest a short list of recommended readings for someone new to the field to quickly grasp the big picture of iris biometrics.
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paper_title: An introduction to biometric recognition

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A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
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paper_title: Image understanding for iris biometrics : A survey

paper_content:

This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. We also suggest a short list of recommended readings for someone new to the field to quickly grasp the big picture of iris biometrics.
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paper_title: Efficient iris recognition by characterizing key local variations

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Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2 255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.
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paper_title: Multilayered Image Processing for Multiscale Harris Corner Detection in Digital Realization

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The PC-based software programming used in complex or luxuriant image processing algorithms is time consuming and resource wasting. As appropriate processing for the image data indeed speedups complicated algorithms, we focus on a crucial case - multilayered processes. In this paper, we gauge deeply into the data flow of multilayered image processing to avoid waiting for the result from every previous steps to access the memory which occurs in many applicable algorithms. Based on combining the parallel and pipelined properties to eliminate unnecessary delays, we propose new visual pipeline architecture and use field programmable gate array to implement our hardware scheme. For verification, the multiscale Harris corner detector in cooperating with shape context and thin-plate splines were combined to complete our real-time experiment of the integrated hardware and software (H/S) system for pattern recognition.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

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The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: Robust iris indexing scheme using geometric hashing of SIFT keypoints

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This paper proposes an efficient indexing scheme for searching large iris biometric database that achieves invariance to similarity transformations, illumination and occlusion. The proposed scheme considers local descriptors as well as relative spatial configuration for claiming identity. To overcome the effect of non-uniform illumination and partial occlusion due to eyelids, local features are extracted from noise independent annular iris image using scale invariant feature transform (SIFT). The detected keypoints are used to index iris database by applying geometric hashing scheme that is robust to similarity transformations as well as occlusion. During iris retrieval, geometric hashed location from query iris image is obtained to access the appropriate bin of hash table and for every entry found there, a vote is casted. The iris images that receive more than certain number of votes are considered as possible candidates. In order to find the potential matches, the keypoint descriptor of the list of possible candidates is matched with the query iris. Since only small portion of database is scanned to find a match it reduces the query retrieval time and improves accuracy. This approach is tested on UBIRIS, BATH, CASIA and IITK iris databases and shows a substantial improvement over exhaustive search technique in terms of time and accuracy.
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paper_title: Implementation of geometric hashing on the Connection Machine

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The authors report on a scalable implementation of geometric hashing on the Connection Machine. Using this implementation, it is possible to recognize models consisting of patterns of points embedded in scenes, independent of translation, rotation, and scale changes. With 1024 models and a scene of 200 points, the implementation yields an execution time of 70 milliseconds for recognition of the one embedded model given a two-point basis set (consisting of a pair of points in the model) on a 64 K-processor CM-2 parallel computer. The algorithm is scalable, yielding an expected execution time that is O(log/sup 2/ M+log S log M) on a Mn/sup 3/-processor hypercube-connected SIMD machine such as the Connection Machine; M is the number of models, n is the number of points per model, S is the number of scene points, and it is assumed that execution proceeds until the first model is discovered. >
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paper_title: Indexing iris images

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Given a query iris image, the goal of indexing is to identify and retrieve a small subset of candidate irides from the database in order to determine a possible match. This can significantly improve the response time of iris recognition systems operating in the identification mode. In this work, we analyze two different approaches to iris indexing. The first technique is based on the analysis of IrisCodes (post-encoding indexing); the second technique is based on the analysis of features extracted from the iris texture (pre-encoding indexing). Experiments on a subset of the publicly available CASIA-IrisV3 database compare the two approaches and illustrate the potential of the proposed indexing methods for large scale iris identification.
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paper_title: Novel Approaches to Improve Robustness, Accuracy and Rapidity of Iris Recognition Systems

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Iris authentication is one of the most successful applications in video analysis and image processing. In this paper, several novel approaches are proposed to improve the overall performance of iris recognition systems. First, this paper proposes a new eyelash detection algorithm based on directional filters, which achieves a low rate of eyelash misclassification. Second, a multiscale and multidirection data fusion method is introduced to reduce the edge effect of wavelet transformation produced by complex segmentation algorithms. Finally, an iris indexing method on the basis of corner detection is presented to accelerate exhausted the 1: N search in a huge iris database. The performance evaluations are carried out on two popular iris databases, and the test results are experimentally more robust and accurate with less elapsed time compared with most existing methods.
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paper_title: An efficient technique for indexing multimodal biometric databases

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This paper proposes an efficient indexing technique which can be used in an identification system with large multimodal biometric database. In this technique, multi-dimensional feature vectors of each trait (iris, signature, ear and face) are normalised and projected to a lower dimensional feature space. The reduced feature vectors are fused at feature level and used to index the database by forming Kd-tree. The performance of the proposed technique is also analysed with the feature vectors of all traits by first fusing them and projecting the fused feature vector to a lower dimensional space, and using it for indexing. Performance is also compared with the indexing based on score-level fusion. The experiment is performed on a multimodal database consisting of 5400 images of 150 subjects (i.e. nine images per subject, per trait). Out of the nine, eight images are used for training and one is used for testing. Our experiment shows that the proposed technique significantly reduces the data retrieval time along with possible error rates.
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paper_title: Circle detection using Hough transform filters

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A novel formulation of the circle Hough transform (CHT) is introduced which enables the detection of circles of different radii to be made using a single parameter space. We formulate the CHT as one or more convolution operations. Comparisons are made between conventional CHT techniques, formulated as filters, and a novel complex vector CHT. This technique exploits both edge orientation and distance from an edge point coded as complex phase. We present results that show that the novel phase and orientation CHT has a positional accuracy of approximately +or-2 pixels at good signal to noise ratios (SNR) down to approximately -16 dB. Using orientation alone, the positional accuracy is poorer falling to approximately +or-10 pixels but at SNR levels of approximately -25 dB. >
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paper_title: How iris recognition works

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The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.
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paper_title: Reducing the false rejection rate of iris recognition using textural and topological features. Int

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This paper presents a novel iris recognition system using 1D log polar Gabor wavelet and Euler numbers. 1D log polar Gabor wavelet is used to extract the textural features, and Euler numbers are used to extract topological features of the iris. The proposed decision strategy uses these features to authenticate an individual's identity while maintaining a low false rejection rate. The algorithm was tested on CASIA iris image database and found to perform better than existing approaches with an overall accuracy of 99.93%. MONG the present biometric traits, iris is found to be the most reliable and accurate (1). The use of human iris as a biometric feature offers many advantages over other biometric features. Iris is the internal human body organ that is visible from outside, but well protected from external modifiers. It has epigenetic formation and it is formed from the individual DNA, but a large part of its final pattern is developed at random. Two eyes from the same individual, although are very similar, contain unique patterns. Similarly, identical twins would exhibit four different iris patterns. These characteristics make it attractive for use as a biometric feature to identify individuals. Pattern recognition and image processing algorithms can be used to extract the unique patterns of iris from an eye image and encode it into an iris template. This iris template contains mathematical representation of the unique information stored in the iris and allows comparisons to be made between templates. Since 1990s, many researchers have worked on this problem. Human iris recognition process is basically divided into four steps, • Localization: Inner and outer boundaries of the iris are extracted.
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paper_title: Local intensity variation analysis for iris recognition

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As all emerging biometric for human identification, iris recognition has received increasing attention in recent years. This paper makes an attempt to reflect shape information of the iris by analyzing local intensity variations of an iris image. In Our framework, a set of one-dimensional (1D) intensity signals is constructed to contain the most important local variations of the original 2D iris image. Gaussian-Hermite moments of Such intensity signals reflect to a large extent their various spatial modes and are used as distinguishing features. A resulting high-dimensional feature vector is mapped into a low-dimensional subspace using Fisher linear discriminant, and then the nearest center classifier based on cosine similarity measure is adopted for classification. Extensive experimental results show that the proposed method is effective and encouraging. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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paper_title: Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing

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This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.
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paper_title: Indexing Iris Biometric Database Using Energy Histogram of DCT Subbands

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The key concern of indexing is to retrieve small portion of database for searching the query. In the proposed paper iris database is indexed using energy histogram. The normalised iris image is divided into subbands using multiresolution DCT transformation. Energy based his- togram is formed for each subband using all the images in the database. Each histogram is divided into fixed size bins to group the iris images having similar energy value. The bin number for each subband is obtained and all subands are traversed in Morton order to form a global key for each image. During database preparation the key is used to traverse the B tree. The images with same key are stored in the same leaf node. For a given query image, the key is generated and tree is traversed to end up to a leaf node. The templates stored at the leaf node are retrieved and compared with the query template to find the best match. The proposed indexing scheme is showing considerably low penetration rate of 0.63%, 0.06% and 0.20% for CASIA, BATH and IITK iris databases respectively.
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paper_title: Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model

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In this paper, we propose a 2-stage preprocessing framework which consists of image enhancement and deformation techniques to increase the verification performance of image-based biometric systems. In the preprocessing framework, first the quality of biometric image is enhanced and then a deformation model is applied to minimize the variation between the two images to be matched. The proposed SVM image quality enhancement algorithm selects good quality regions from different globally enhanced images and combines them to generate a single high-quality feature-rich image. The proposed deformation algorithm is based on phase congruency information and locally minimizes the variations between two images while retaining the features required for recognition. The proposed algorithms are validated using face and iris biometrics as the two case studies. For performance evaluation, different face and iris recognition algorithms are chosen and the verification accuracy is computed using non-homogenous face and iris databases. Experimental results show that the performance of face and iris recognition algorithms are significantly improved when the input images are preprocessed using the proposed enhancement and deformation algorithms.
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paper_title: Iris-Biometric Hash Generation for Biometric Database Indexing

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Performing identification on large-scale biometric databases requires an exhaustive linear search. Since biometric data does not have any natural sorting order, indexing databases, in order to minimize the response time of the system, represents a great challenge. In this work we propose a biometric hash generation technique for the purpose of biometric database indexing, applied to iris biometrics. Experimental results demonstrate that the presented approach highly accelerates biometric identification.
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paper_title: A novel iris database indexing method using the iris color

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In a very large iris database, the design of an effective iris indexing method reduces the computational complexity of feature matching process. This paper presents a new iris indexing technique based on the iris color for noisy iris images. The proposed method using the chrominance components achieves high indexing performance in defocused, reflection-contained and eyelid-occluded iris images. The performance measures such as the hit rate and the penetration rate computed on a recently created noisy iris database, namely UBIRIS (Proenca and Alexandre, 2005), show the effectiveness of the iris color to index very large iris databases.
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paper_title: An iris retrieval technique based on color and texture

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This paper proposes an efficient indexing scheme that can be used for retrieval from a large iris database. For a given color iris query image, the proposed indexing scheme makes use of iris color to determine an index and uses this index to reduce the search space in the large iris database. Further, for query q, the retrieval technique uses iris texture to find the top best match from the reduced search space. The proposed technique has been tested on two publicly available color iris databases, viz UPOL [10] of 384 images and UBIRIS [13] of 1860 fully noisy images and is found to be robust against change in gaze, illumination, partial occlusions and scale. In both the databases, the test reveals that a small subspace is sufficient to achieve 100% hitrate for the top best match under various scales, illumination and partial occlusion. The performance of the proposed indexing scheme is analyzed against the group based color indexing scheme proposed in [14]. The results show that proposed indexing scheme is performing better as compared to group based color indexing scheme with respect to hitrate, penetration rate and CMC curve.
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Title: Iris Indexing Techniques: A Review

Section 1: INTRODUCTION
Description 1: This section provides an overview of traditional and biometric identification methods, emphasizing the importance of iris recognition as a reliable biometric trait. It outlines the paper's organization and the need for efficient indexing techniques in large-scale databases.

Section 2: Iris basics
Description 2: This section explains the anatomy and unique characteristics of the human iris, highlighting why it is an effective feature for biometric identification.

Section 3: Iris Feature Extraction
Description 3: This section details various methods used to extract features from the iris, including segmentation, normalization, and encoding processes, particularly focusing on Daugman's method.

Section 4: IRIS INDEXING TECHNIQUES
Description 4: This section reviews various iris indexing techniques, categorized based on iris code, iris texture, and iris color. It discusses methodologies, their effectiveness, and challenges faced.

Section 5: Performance Evaluation Metrics and Iris Databases
Description 5: This section describes the different metrics used to evaluate the performance of iris indexing techniques and provides an overview of commonly used iris databases.

Section 6: CONCLUDING REMARKS
Description 6: This section summarizes the key findings from the review, noting the prevalent methods and their shortcomings, while suggesting areas for future research in iris indexing for large databases.