A review of iris recognition algorithms
---
paper_title: How iris recognition works

paper_content:

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.
---
paper_title: How iris recognition works

paper_content:

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.
---
paper_title: High confidence visual recognition of persons by a test of statistical independence

paper_content:

A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/. >
---
paper_title: Iris recognition: an emerging biometric technology

paper_content:

In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. As technology advances and information and intellectual properties are wanted by many unauthorized personnel. As a result many organizations have being searching ways for more secure authentication methods for the user access. In network security there is a vital emphasis on the automatic personal identification. Due to its inherent advantages biometric based verification especially iris identification is gaining a lot of attention. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.
---
paper_title: Iris recognition using circular symmetric filters

paper_content:

Proposes a method for personal identification based on iris recognition. The method consists of three major components: image preprocessing, feature extraction and classifier design. A bank of circular symmetric filters is used to capture local iris characteristics to form a fixed length feature vector. In iris matching, an efficient approach called nearest feature line (NFL) is used. Constraints are imposed on the original NFL method to improve performance. Experimental results show that the proposed method has an encouraging performance.
---
paper_title: Locating the iris: A first step to registration and identification

paper_content:

This paper presents a method for locating the iris borders in images of the cornea. It uses a combination of a priori information, statistics and active contours to both find the iris borders and assess its own success or failure. When applied to 107 images from 11 different people with varying eye colours including light blue and dark brown the algorithm reports 7 images for which it failed to find the borders, one of which was a false negative. In the other 100 images, the algorithm located the iris borders with average estimated errors of 5.0 pixels for the left iris-sclera border, 3.4 pixels for the right iris-sclera border and 2.1 pixels for the pupil-iris border. There were no false positive results
---
paper_title: Efficient Iris Recognition through Improvement of Feature Vector and Classifier

paper_content:

In this paper, we propose an efficient method for personal identification by analyzing iris patterns that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform, and two straightforward but efficient mechanisms for a competitive learning method such as a weight vector initialization and the winner selection. With all of these novel mechanisms, the experimental results showed that the proposed system could be used for personal identification in an efficient and effective manner.
---
paper_title: Iris recognition using collarette boundary localization

paper_content:

There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years, and biometrics has become an important technology for security. The iris recognition system consists of four-process: image acquisition, preprocessing, feature extraction and identification or verification. In this paper, we propose the methods for localizing the iris area between the inner boundary and the collarette boundary, to remove unnecessary areas and to increase the recognition rate. For finding the collarette boundary, histogram equalization and a high pass filter, after using an one-dimensional DFT, are applied to the image. The collarette boundary is found using statistical information from the image, which removes low-frequencies, and, finally, the iris is localized between the inner boundary and the collarette boundary. The iris is localized by two kinds of methods, and the recognition rates were compared. The recognition rate was evaluated by using DWT and SVM. These show that the iris localization by the proposed methods contains more information than the previous methods and improves the recognition rate.
---
paper_title: An Efficient OneDimensional Fractal Analysis for Iris Recognition

paper_content:

An iris recognition system is proposed in this paper. This system implements (a) the combination of proposed black hole search method and integro-differential operators for iris localization, (b) one-dimensional fractal analysis for feature extraction and (c) dissimilarity operator for matching. The black hole search method achieves 100% accuracy of pupil detection for both CASIA and MMU Iris databases. Experiment results show that the proposed system has an encouraging result with low EER to 4.63% in CASIA iris database and 4.17% in MMU iris database. It provides an alternative solution for iris recognition and acts as a spring board for further investigation.
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paper_title: Reliable Iris Localization Method With Application To Iris Recognition In Near Infrared Light

paper_content:

Iris recognition is accepted as one of the best biometric method. Implementing this method to the practical system requires the special preprocessing where the iris localization plays a crucial role. Iris localization consists of finding the iris boundaries as well as eyelids. In this paper a simple iris localization algorithm is proposed based on iris image segmentation with histogram analysis. Proposed solution was applied for images taken under near infrared light
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paper_title: A fast and robust iris localization method based on texture segmentation

paper_content:

With the development of the current networked society, personal identification based on biometrics has received more and more attention. Iris recognition has a satisfying performance due to its high reliability and non-invasion. In an iris recognition system, preprocessing, especially iris localization plays a very important role. The speed and performance of an iris recognition system is crucial and it is limited by the results of iris localization to a great extent. Iris localization includes finding the iris boundaries (inner and outer) and the eyelids (lower and upper). In this paper, we propose an iris localization algorithm based on texture segmentation. First, we use the information of low frequency of wavelet transform of the iris image for pupil segmentation and localize the iris with a differential integral operator. Then the upper eyelid edge is detected after eyelash is segmented. Finally, the lower eyelid is localized using parabolic curve fitting based on gray value segmentation. Extensive experimental results show that the algorithm has satisfying performance and good robustness.
---
paper_title: A new iris segmentation method for recognition

paper_content:

As the first stage, iris segmentation is very important for an iris recognition system. If the iris regions were not correctly segmented, there would possibly exist four kinds of noises in segmented iris regions: eyelashes, eyelids, reflections and pupil, which result in poor recognition performance. This paper proposes a new noise-removing approach based on the fusion of edge and region information. The whole procedure includes three steps: 1) rough localization and normalization, 2) edge information extraction based on phase congruency, and 3) the infusion of edge and region information. Experimental results on a set of 2,096 images show that the proposed method has encouraging performance for improving the recognition accuracy.
---
paper_title: Accurate iris segmentation based on novel reflection and eyelash detection model

paper_content:

The authors propose a novel noise detection model for accurate segmentation of an iris. Eyelash, eyelid and reflection are three main noises. Eyelid has already been solved by a traditional eye model; however, eyelash and reflection have not been tackled. To determinate a pixel in an eyelash, the model presented follows the three criteria: 1) separable eyelash condition; 2) non-informative condition; and 3) connective criterion. The first and second conditions handle separable and multiple eyelashes respectively. The last criterion avoids misclassification of strong iris texture as a single and separable eyelash. For reflection, strong reflection points are detected by a threshold and the weak reflection points around the strong points are determined by connective criterion and statistical test. A number of images are selected to evaluate the accuracy and necessity of our noise detection model and the results are encouraging.
---
paper_title: How iris recognition works

paper_content:

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.
---
paper_title: High confidence visual recognition of persons by a test of statistical independence

paper_content:

A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/. >
---
paper_title: Biometric personal identification based on iris patterns

paper_content:

A new system for personal identification based on iris patterns is presented in this paper. It is composed of iris image acquisition, image preprocessing, feature extraction and classifier design. The algorithm for iris feature extraction is based on texture analysis using multichannel Gabor filtering and wavelet transform. Compared with existing methods, our method employs the rich 2D information of the iris and is translation, rotation, and scale invariant.
---
paper_title: A new iris segmentation method for recognition

paper_content:

As the first stage, iris segmentation is very important for an iris recognition system. If the iris regions were not correctly segmented, there would possibly exist four kinds of noises in segmented iris regions: eyelashes, eyelids, reflections and pupil, which result in poor recognition performance. This paper proposes a new noise-removing approach based on the fusion of edge and region information. The whole procedure includes three steps: 1) rough localization and normalization, 2) edge information extraction based on phase congruency, and 3) the infusion of edge and region information. Experimental results on a set of 2,096 images show that the proposed method has encouraging performance for improving the recognition accuracy.
---
paper_title: Iris recognition using circular symmetric filters

paper_content:

Proposes a method for personal identification based on iris recognition. The method consists of three major components: image preprocessing, feature extraction and classifier design. A bank of circular symmetric filters is used to capture local iris characteristics to form a fixed length feature vector. In iris matching, an efficient approach called nearest feature line (NFL) is used. Constraints are imposed on the original NFL method to improve performance. Experimental results show that the proposed method has an encouraging performance.
---
paper_title: How iris recognition works

paper_content:

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.
---
paper_title: High confidence visual recognition of persons by a test of statistical independence

paper_content:

A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/. >
---
paper_title: The importance of phase in signals

paper_content:

Abstract : Experimental results supporting the importance of phase attempts at explaining the importance of phase algorithms for reconstructing a signal from its phase potential applications. (Author)
---
paper_title: Efficient Iris Recognition through Improvement of Feature Vector and Classifier

paper_content:

In this paper, we propose an efficient method for personal identification by analyzing iris patterns that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform, and two straightforward but efficient mechanisms for a competitive learning method such as a weight vector initialization and the winner selection. With all of these novel mechanisms, the experimental results showed that the proposed system could be used for personal identification in an efficient and effective manner.
---
paper_title: A Novel Iris Recognition System Using Morphological Edge Detector and Wavelet Phase Features

paper_content:

The present invention features a valve train for inducing reciprocative motion in a valve which includes a lever pivotally mounted at one end thereof, a rocker arm which engages the lever to define a fulcrum point therebetween, and which engages a valve at one end thereof, a first cam which engages the other end of the rocker arm, a second cam which engages the top of the lever and a mechanism which guidingly interconnects the rocker arm, at a point intermediate of the ends thereof, with the lever for preventing relative slip therebetween and for ensuring that a cam following portion of the rocker arm smoothly follows the first cam.
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paper_title: Iris-based biometric recognition using dyadic wavelet transform

paper_content:

A biometric identification system, based on the processing of the human iris by the dyadic wavelet transform, has been introduced. The procedure to obtain an iris signature of 256 bits has been described, as well as the feature extraction block and the verification system. The results have shown that the system can achieve high rates of security.
---
paper_title: Iris recognition: an emerging biometric technology

paper_content:

In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. As technology advances and information and intellectual properties are wanted by many unauthorized personnel. As a result many organizations have being searching ways for more secure authentication methods for the user access. In network security there is a vital emphasis on the automatic personal identification. Due to its inherent advantages biometric based verification especially iris identification is gaining a lot of attention. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.
---
paper_title: A system for automated iris recognition

paper_content:

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endeavour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body its appearance is amenable to remote examination with the aid of a computer vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study where the system exhibits flawless performance in the evaluation of 520 iris images. >
---
paper_title: Efficient iris recognition by characterizing key local variations

paper_content:

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.
---
paper_title: Person Identification Technique Using Human Iris Recognition

paper_content:

The biometric person authentication technique based on the pattern of the human iris is well suited to be applied to any access control system requiring a high level of security. This paper examines a new iris recognition system that implements (i) gradient decomposed Hough transform / integro-differential operators combination for iris localization and (ii) the "analytic image" concept (2D Hilbert transform) to extract pertinent information from iris texture. All these image-processing algorithms have been validated on noised real iris images database. The proposed innovative technique is computationally effective as well as reliable in terms of recognition rates.
---
paper_title: How iris recognition works

paper_content:

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.
---
paper_title: High confidence visual recognition of persons by a test of statistical independence

paper_content:

A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/. >
---
paper_title: Biometric personal identification based on iris patterns

paper_content:

A new system for personal identification based on iris patterns is presented in this paper. It is composed of iris image acquisition, image preprocessing, feature extraction and classifier design. The algorithm for iris feature extraction is based on texture analysis using multichannel Gabor filtering and wavelet transform. Compared with existing methods, our method employs the rich 2D information of the iris and is translation, rotation, and scale invariant.
---
paper_title: Iris recognition: an emerging biometric technology

paper_content:

In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. As technology advances and information and intellectual properties are wanted by many unauthorized personnel. As a result many organizations have being searching ways for more secure authentication methods for the user access. In network security there is a vital emphasis on the automatic personal identification. Due to its inherent advantages biometric based verification especially iris identification is gaining a lot of attention. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.
---
paper_title: A system for automated iris recognition

paper_content:

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endeavour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body its appearance is amenable to remote examination with the aid of a computer vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study where the system exhibits flawless performance in the evaluation of 520 iris images. >
---
paper_title: Face recognition using the nearest feature line method

paper_content:

We propose a classification method, called the nearest feature line (NFL), for face recognition. Any two feature points of the same class (person) are generalized by the feature line (FL) passing through the two points. The derived FL can capture more variations of face images than the original points and thus expands the capacity of the available database. The classification is based on the nearest distance from the query feature point to each FL. With a combined face database, the NFL error rate is about 43.7-65.4% of that of the standard eigenface method. Moreover, the NFL achieves the lowest error rate reported to date for the ORL face database.
---
paper_title: Iris recognition using circular symmetric filters

paper_content:

Proposes a method for personal identification based on iris recognition. The method consists of three major components: image preprocessing, feature extraction and classifier design. A bank of circular symmetric filters is used to capture local iris characteristics to form a fixed length feature vector. In iris matching, an efficient approach called nearest feature line (NFL) is used. Constraints are imposed on the original NFL method to improve performance. Experimental results show that the proposed method has an encouraging performance.
---

Title: A review of iris recognition algorithms

Section 1: Introduction
Description 1: This section provides an overview of biometric identification, focusing on the uniqueness and reliability of using iris recognition.

Section 2: Image preprocessing
Description 2: This section details the steps involved in preprocessing iris images, including iris localization, normalization, and image enhancement.

Section 3: Iris localization
Description 3: This section discusses various iris localization algorithms and techniques, including Integro-differential operator, Hough transform, Discrete Circular Active Contour, Bisection method, and Black hole search method.

Section 4: Eyelid and eyelash detection
Description 4: This section covers methods to detect and remove occlusions caused by eyelids and eyelashes, using texture segmentation, Daubechies wavelets, Gabor filters, and other techniques.

Section 5: Iris normalization
Description 5: This section describes the normalization process to account for variations in iris size and pupil dilation, explaining the Homogenous rubber sheet model.

Section 6: Image enhancement
Description 6: This section explains image enhancement techniques applied to the normalized iris image to improve contrast and illumination uniformity.

Section 7: Feature extraction
Description 7: This section outlines various texture analysis methods for extracting significant features from the iris image, including Gabor filters, Wavelet transform, Laplacian of Gaussian filter, and more.

Section 8: Template matching
Description 8: This section discusses different metrics for matching iris templates, such as Hamming distance, Weighted Euclidean distance, Normalized correlation, and Nearest feature line.

Section 9: Discussion
Description 9: This section evaluates the impact of noise on iris recognition performance and the limitations of current research, advocating for comprehensive noise compensation and larger, diverse datasets.

Section 10: Conclusion
Description 10: This section summarizes the review, emphasizing the reliability of iris recognition and the potential for algorithm optimization in various applications.