A Bayesian Approach to Deformed Pattern Matching of Iris Images

Thornton, J. Savvides, M. Kumar, V. Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PAThis paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on Publication Date: April 2007 Volume: 29 , Issue: 4 On page(s): 596 - 606 ISSN: 0162-8828 INSPEC Accession Number:9370982 Digital Object Identifier: 10.1109/TPAMI.2007.1006 Current Version Published: 2007-02-20 PubMed ID: 17299217

Abstract

We describe a general probabilistic framework for matching patterns that experience in-plane nonlinear deformations, such as iris patterns. Given a pair of images, we derive a maximum a posteriori probability (MAP) estimate of the parameters of the relative deformation between them. Our estimation process accomplishes two things simultaneously: it normalizes for pattern warping and it returns a distortion-tolerant similarity metric which can be used for matching two nonlinearly deformed image patterns. The prior probability of the deformation parameters is specific to the pattern-type and, therefore, should result in more accurate matching than an arbitrary general distribution. We show that the proposed method is very well suited for handling iris biometrics, applying it to two databases of iris images which contain real instances of warped patterns. We demonstrate a significant improvement in matching accuracy using the proposed deformed Bayesian matching methodology. We also show that the additional computation required to estimate the deformation is relatively inexpensive, making it suitable for real-time applications

2. Iris Recognition with Multi-Scale Edge-Type Matching

Chia-Te Chou Sheng-Wen Shih Wen-Shiung Chen Cheng, V.W. Dept. of Comput. Sci. & Inf. Eng., National Chi Nan Univ., NantouThis paper appears in: Pattern Recognition, 2006. ICPR 2006. 18th International Conference on Publication Date: 0-0 0 Volume: 4 On page(s): 545 - 548 Location: Hong Kong ISSN: 1051-4651

ISBN: 0-7695-2521-0 INSPEC Accession Number:9164300 Digital Object Identifier: 10.1109/ICPR.2006.728 Current Version Published: 2006-09-18

AbstractIn this paper, we propose a novel descriptor which characterizes an iris pattern with multi-scale step/ridge edge-type (ET) maps. The ET maps are determined with the derivative of Gaussian (DoG) and the Laplacian of Gaussian (LoG) filters. There are two major advantages of our approach. First, both the feature extraction and the pattern classification are simple and efficient. The iris pattern classification is accomplished by ET matching. The matching of each ET flag can be regarded as a weak classifier and the final decision is based on the vote of each weak classifier. Second, the number of free filter parameters is only three, and hence they can be easily determined. Furthermore, we propose a method for designing the parameters of the filters with the genetic algorithm. The experimental results show that our approach can achieve a recognition rate of 99.98% which is comparable to that of the Gabor filter approach.

3.Iris Recognition Based on Matching Pursuits

Yueh-Shiun Lee Jinn Ho Wen-Liang Hwang Chung-Lin Huang Dept. of Electr. Eng., Nat. Tsing Hua Univ., HsinchuThis paper appears in: Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP '08 International Conference on Publication Date: 15-17 Aug. 2008 On page(s): 183 - 187 Location: Harbin ISBN: 978-0-7695-3278-3 INSPEC Accession Number:10181817 Digital Object Identifier: 10.1109/IIH-MSP.2008.331 Current Version Published: 2008-08-22

AbstractWe propose a novel dynamic programming matching pursuit (DPMP) algorithm for iris recognition. The method modifies the matching pursuit (MP) algorithm to select the most representative atoms for the iris recognition. In the experiments, we demonstrate that our system has (1) better performance for both personal identification and verification, and (2) a better ROC curve, (3) less computation than the conventional MP-based iris recognition.

4.Transforming Traditional Iris Recognition Systems to Work in Nonideal Situations

Zhou, Z. Du, Y. Belcher, C. This paper appears in: Industrial Electronics, IEEE Transactions on Publication Date: Aug. 2009 Volume: 56 , Issue: 8 On page(s): 3203 - 3213 ISSN: 0278-0046 Digital Object Identifier: 10.1109/TIE.2009.2024653 First Published: 2009-06-10 Current Version Published: 2009-07-24

AbstractUnder a nonideal situation, the image quality may vary. As a result, the traditional iris recognition systems would not work well. However, these kinds of iris recognition systems have been widely deployed in law enforcement and homeland security. It will be desirable to transform the traditional systems to perform in nonideal situations without a costly update. In this paper, we propose a method that upgrades the traditional iris recognition system to work on nonideal situations. The proposed method takes into consideration not only the effect of image quality but also the segmentation accuracy. It employs video-based image-processing techniques to quickly identify and eliminate the bad quality images from iris videos for further processing. The proposed method is tested on public databases using in-house recognition algorithms and also evaluated using a commercialized system. The research results show that the proposed methods can be used to improve the performance of iris recognition systems in a nonideal situation.

5.Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments

Matey, J.R. Naroditsky, O. Hanna, K. Kolczynski, R. LoIacono, D.J. Mangru, S. Tinker, M. Zappia, T.M. Zhao, W.Y. Sarnoff Corp., Princeton, NJThis paper appears in: Proceedings of the IEEE Publication Date: Nov. 2006 Volume: 94 , Issue: 11 On page(s): 1936 - 1947 ISSN: 0018-9219 INSPEC Accession Number:9271390 Digital Object Identifier: 10.1109/JPROC.2006.884091 Current Version Published: 2007-01-08

Abstract

Iris recognition is one of the most powerful techniques for biometric identification ever developed. Commercial systems based on the algorithms developed by John Daugman have been available since 1995 and have been used in a variety of practical applications. However, all currently available systems impose substantial constraints on subject position and motion during the recognition process. These constraints are largely driven by the image acquisition process, rather than the particular pattern-matching algorithm used for the recognition process. In this paper we present results of our efforts to substantially reduce constraints on position and motion by means of a new image acquisition system based on high-resolution cameras, video synchronized strobed illumination, and specularity based image segmentation. We discuss the design tradeoffs we made in developing the system and the performance we have been able to achieve when the image acquisition system is combined with a standard iris recognition algorithm. The Iris on the Move (IOM) system is the first system to enable capture of iris images of sufficient quality for iris recognition while the subject is moving at a normal walking pace through a minimally confining portal

6. Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing

Vatsa, M. Singh, R. Noore, A. Lane Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WVThis paper appears in: Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on Publication Date: Aug. 2008 Volume: 38 , Issue: 4 On page(s): 1021 - 1035 ISSN: 1083-4419 INSPEC Accession Number:10117709 Digital Object Identifier: 10.1109/TSMCB.2008.922059 First Published: 2008-05-07 Current Version Published: 2008-07-16 PubMed ID: 18632394

AbstractThis 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.

7.Probing the Uniqueness and Randomness of IrisCodes: Results From 200 Billion Iris Pair Comparisons

Daugman, J. Comput. Lab., Cambridge Univ.This paper appears in: Proceedings of the IEEE Publication Date: Nov. 2006 Volume: 94 , Issue: 11 On page(s): 1927 - 1935 ISSN: 0018-9219 INSPEC Accession Number:9271389 Digital Object Identifier: 10.1109/JPROC.2006.884092 Current Version Published: 2007-01-08

AbstractRecent large-scale deployments of iris recognition for border-crossing controls enable critical assessment of the robustness of this technology against making false matches, since vast numbers of cross comparisons become possible within large databases. This paper presents results from the 200 billion iris cross comparisons that could be performed within a database of 632 500 different iris images, spanning 152 nationalities. Each iris pattern was encoded into a phase sequence of 2048 bits using the Daugman algorithms. Empirically analyzing the tail of the resulting distribution of similarity scores enables specification of decision thresholds, and prediction of performance, of the iris recognition algorithms if deployed in identification mode on national scales.

8.Effect of Severe Image Compression on Iris Recognition Performance

Daugman, J. Downing, C. Univ. of Cambridge, CambridgeThis paper appears in: Information Forensics and Security, IEEE Transactions on Publication Date: March 2008 Volume: 3 , Issue: 1 On page(s): 52 - 61 ISSN: 1556-6013

INSPEC Accession Number:9873248 Digital Object Identifier: 10.1109/TIFS.2007.916009 Current Version Published: 2008-02-12

AbstractWe investigate three schemes for severe compression of iris images in order to assess what their impact would be on recognition performance of the algorithms deployed today for identifying people by this biometric feature. Currently, standard iris images are 600 times larger than the IrisCode templates computed from them for database storage and search; but it is administratively desired that iris data should be stored, transmitted, and embedded in media in the form of images rather than as templates computed with proprietary algorithms. To reconcile that goal with its implications for bandwidth and storage, we present schemes that combine region-of-interest isolation with JPEG and JPEG2000 compression at severe levels, and we test them using a publicly available database of iris images. We show that it is possible to compress iris images to as little as 2000 bytes with minimal impact on recognition performance. Only some 2% to 3% of the bits in the IrisCode templates are changed by such severe image compression, and we calculate the entropy per code bit introduced by each compression scheme. Error tradeoff curve metrics document very good recognition performance despite this reduction in data size by a net factor of 150, approaching a convergence of image data size and template size.

9.Real-Time Image Restoration for Iris Recognition Systems

Byung Jun Kang Kang Ryoung Park Sangmyung Univ., SeoulThis paper appears in: Systems, Man, and Cybernetics, Part B, IEEE Transactions on Publication Date: Dec. 2007 Volume: 37 , Issue: 6 On page(s): 1555 - 1566 ISSN: 1083-4419 INSPEC Accession Number:9674187 Digital Object Identifier: 10.1109/TSMCB.2007.907042 Current Version Published: 2007-11-19 PubMed ID: 18179073

AbstractIn the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small

depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the camera's DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: (1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; (2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; (3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; (4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and (5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.

10.New Methods in Iris Recognition

Daugman, J. Cambridge Univ., CambridgeThis paper appears in: Systems, Man, and Cybernetics, Part B, IEEE Transactions on Publication Date: Oct. 2007 Volume: 37 , Issue: 5 On page(s): 1167 - 1175 ISSN: 1083-4419 INSPEC Accession Number:9624784 Digital Object Identifier: 10.1109/TSMCB.2007.903540 Current Version Published: 2007-09-24 PubMed ID: 17926700

AbstractThis paper presents the following four advances in iris recognition: 1) more disciplined methods for detecting and faithfully modeling the iris inner and outer boundaries with active contours, leading to more flexible embedded coordinate systems; 2) Fourier-based methods for solving problems in iris trigonometry and projective geometry, allowing off-axis gaze to be handled by detecting it and ldquorotatingrdquo the eye into orthographic perspective; 3) statistical inference methods for detecting and excluding eyelashes; and 4) exploration of score normalizations, depending on the amount of iris data that is available in images and the required scale of database search. Statistical results are presented based on 200 billion iris cross-comparisons that were generated from 632 500 irises in the United Arab Emirates database to analyze the normalization issues raised in different regions of receiver operating characteristic curves.

11. Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures

Proenca, H. Alexandre, L.A. Departamento de Inf., Universidade da Beira Interior, CovilhaThis paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on Publication Date: April 2007 Volume: 29 , Issue: 4 On page(s): 607 - 612 ISSN: 0162-8828 INSPEC Accession Number:9370983 Digital Object Identifier: 10.1109/TPAMI.2007.1016 Current Version Published: 2007-02-20 PubMed ID: 17299218

AbstractThis 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.

12. DCT-Based Iris Recognition

Monro, D.M. Rakshit, S. Dexin Zhang Dept. of Electron. & Electr. Eng., Univ. of BathThis paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on Publication Date: April 2007 Volume: 29 , Issue: 4 On page(s): 586 - 595 ISSN: 0162-8828 INSPEC Accession Number:9370981 Digital Object Identifier: 10.1109/TPAMI.2007.1002 Current Version Published: 2007-02-20 PubMed ID: 17299216

AbstractThis paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent correct recognition rate (CRR) and perfect receiver-operating characteristic (ROC) curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the false acceptance rate (FAR) and false rejection rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical equal error rate (EER) is predicted to be as low as 2.59 times 10-1 available data sets.

13.Contactless Autofeedback Iris Capture Design

Xiaofu He Jingqi Yan Guangyu Chen Pengfei Shi Inst. of Image Process. & Pattern Recognition, Shanghai Jiao Tong Univ., ShanghaiThis paper appears in: Instrumentation and Measurement, IEEE Transactions on Publication Date: July 2008 Volume: 57 , Issue: 7 On page(s): 1369 - 1375 Location: Braunschweig, Germany ISSN: 0018-9456 INSPEC Accession Number:10006693 Digital Object Identifier: 10.1109/TIM.2007.915437 Current Version Published: 2008-05-28

AbstractAutomated iris recognition is one of the most reliable biometrics in terms of identification and verification performance. One of the major challenges for automated iris recognition is to capture a high-quality image of the iris since system performance is greatly affected by poor-quality imaging. This paper describes the design and implementation of a high-quality imaging device for iris acquisition, which consists of the following four parts: 1) capture unit; 2) illumination unit; 3) feedback unit; and 4) pitching outfit. Using the proposed device for iris acquisition, it is feasible to obtain real-time, high-quality iris images, given the fact that it is also user friendly. Recognition experiments in an iris database that contains 6550 images captured by the new device are also presented in this paper.

14. A real-time focusing algorithm for iris recognition camera

Kang Ryoung Park Jaihie Kim Div. of Media Technol., SangMyung Univ., Seoul, South KoreaThis paper appears in: Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on Publication Date: Aug. 2005 Volume: 35 , Issue: 3 On page(s): 441 - 444 ISSN: 1094-6977 Digital Object Identifier: 10.1109/TSMCC.2005.848168 Current Version Published: 2005-07-25

AbstractFor fast iris recognition, it is very important to capture the user's focused eye image at fast speed. Previous researchers have used the focusing method which has been applied to general landscape scenes without considering the characteristics of the iris image. So, they take much focusing time, especially in the case of the user with glasses. To overcome such problems, we propose a new iris image acquisition method to capture focused eye images at very fast speed based on corneal specular reflection. Experimental results show that the focusing time for both users with and without glasses averages 480 ms, and we conclude that our method can be used for the real-time iris recognition camera.

15.Quality assessment of biometric systems: a comprehensive perspective based on accuracy and performance measurement

Gamassi, M. Lazzaroni, M. Misino, M. Piuri, V. Sana, D. Scotti, F. Dipt. di Tecnologie dell'Informazione, Univ. degli Studi di Milano, Crema, ItalyThis paper appears in: Instrumentation and Measurement, IEEE Transactions on Publication Date: Aug. 2005 Volume: 54 , Issue: 4 On page(s): 1489 - 1496 ISSN: 0018-9456 Digital Object Identifier: 10.1109/TIM.2005.851087 Current Version Published: 2005-07-18

AbstractDespite the efforts of the international biometric community, the measurement of the accuracy of a biometric system is far from being completely investigated and, eventually, standardized. This paper presents a critical analysis of the accuracy and performance measurement of a biometric system. Current approaches to the problem and procedural

error have been described and criticized. Finally, a methodology for the measurement of the accuracy of biometric systems with nonsymmetric matching function will be proposed and discussed.

16. A robust hybrid iris localization technique

Ziauddin, S. Dailey, M.N. Comput. Sci. & Inf. Manage., Asian Inst. of Technol., PathumthaniThis paper appears in: Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009. ECTI-CON 2009. 6th International Conference on Publication Date: 6-9 May 2009 Volume: 02 On page(s): 1058 - 1061 Location: Pattaya, Chonburi ISBN: 978-1-4244-3387-2 Digital Object Identifier: 10.1109/ECTICON.2009.5137227 Current Version Published: 2009-06-26

AbstractIn this paper, a new iris localization method is presented. An iris recognition system acquires a human eye image, segments the iris region from the rest of the image, normalizes this segmented image and encodes features to get a compact iris template. Performance of all subsequent stages in an iris recognition system is highly dependent on correct detection of pupil-iris and iris-sclera boundaries in the eye images. In this paper, we present one such system which finds pupil boundary using image gray levels but uses edge detection and circular Hough transform to locate iris boundary. We introduce a number of optimizations to traditional Hough transform based methods. Experimental evaluation shows that the proposed system is accurate and efficient enough for real life applications.

17. Using Iris Recognition Algorithm, Detecting Cholesterol Presence

Ramlee, R.A. Ranjit, S. Fac. of Electron. Eng. & Comput. Eng., Univ. Teknikal Malaysia, Ayer KerohThis paper appears in: Information Management and Engineering, 2009. ICIME '09. International Conference on Publication Date: 3-5 April 2009 On page(s): 714 - 717 Location: Kuala Lumpur ISBN: 978-0-7695-3595-1 Digital Object Identifier: 10.1109/ICIME.2009.61 Current Version Published: 2009-06-19

AbstractThe objective of this paper is to use existing iris recognition methods as an alternative method to detect the presence of cholesterol in blood vessel. This research adopts John Dugan's and Libor Mask's iris recognition methods and alternative medicine, iridology. Based on the iris recognition methods and iridology chart, a Matlab program has been created to detect the present of cholesterol in human body.

18. Discriminant iris feature and support vector machines for iris recognition

Byungjun Son; Hyunsuk Won; Gyundo Kee; Yillbyung LeeImage Processing, 2004. ICIP apos;04. 2004 International Conference onVolume 2, Issue , 24-27 Oct. 2004 Page(s): 865 - 868 Vol.2Digital Object Identifier   10.1109/ICIP.2004.1419436

Summary: In an iris recognition system, the size of the feature set is normally large. As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient iris recognition. In this paper. we present one of the major discriminative learning methods, namely, Direct Linear Discriminant Analysis (DLDA). Also, we apply the multiresolution wavelet transform to extract the unique feature from the acquired iris image and to decrease the complexity of computation when using DLDA. The Support Vector Machines (SVM) approach for comparing the similarity between the similar and different irises can be assessed to have the feature's discriminative power. In the experiments, we have showed that that the proposed method for human iris gave a efficient way of representing iris patterns.

20.Automatic people identification on the basis of iris pattern image processing and preliminary analysisJablonski, P.; Szewczyk, R.; Kulesza, Z.; Napieralski, A.; Moreno, M.; Cabestany, J.Microelectronics, 2002. MIEL 2002. 23rd International Conference onVolume 2, Issue , 2002 Page(s):687 - 690Digital Object Identifier   10.1109/MIEL.2002.1003351

Summary:People identification gains more importance in life and consequently more concern in science. This paper tries to present a simple way to solve the problem through a basic analysis of the human iris. Main predicaments and strategies to overcome them are outlined and a direction of future activities leading to a complete and simple identification system is pointed out

21. Noise removal and impainting model for iris imageJunzhou Huang; Yunhong Wang; Jiali Cui; Tieniu TanImage Processing, 2004. ICIP apos;04. 2004 International Conference on

Volume 2, Issue , 24-27 Oct. 2004 Page(s): 869 - 872 Vol.2Digital Object Identifier   10.1109/ICIP.2004.1419437

Summary: Noise removal is an important problem for iris recognition. If the iris regions were not correctly segmented in iris images, segmented iris regions possibly include noises, namely eyelashes, eyelids, reflections and pupil. Noises influence the features of both noise regions and their neighboring regions, which will result in poor recognition performance. To solve this problem, this paper proposes a method for removing noises and impainting iris images. The whole procedure includes three steps: 1) localization and normalization, 2) noise removal based on phase congruency and 3) iris image impainting. A series of experiments show that the proposed method has encouraging performance for improving the recognition accuracy.

21. Iris Compression for Cryptographically Secure Person IdentificationIn this paper we propose EyeCerts, a biometric system for identification of people which achieves off-line verification of certified, cryptographically secure documents. An EyeCert is a printed document which certifies the association of a given text with a biometric feature - a compressed version of a human iris in this work. As a central component of the yeCert system, we present an iris analysis technique that extracts and compresses the unique features of a given iris using limited storage. The compressed features should be at maximal distance with respect to a reference iris image database. The iris analysis algorithm performs several steps in three main phases: (i) it detects the human iris, (ii) it converts the isolated iris using a modified Fourier-Mellin transform into a standard domain where the common radial patterns of the human iris are concisely represented, and (iii) it optimally selects, aligns, and near-optimally compresses the most distinctive transform coefficients for each individual user. Using a low quality imaging system (sub-US$100) and developed and readily available low complexity processing techniques, the overall system is shown to have probabilities of false negative and false positive on the order of 10-5.

22. Identification of individuals through the morphological processing of the iris

de Mira, J., Jr.; Mayer, J.Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference onVolume 1, Issue , 14-17 Sept. 2003 Page(s): I - 341-4 vol.1Digital Object Identifier   10.1109/ICIP.2003.1246968Summary: A new approach based on morphological operators is presented for identification of individuals by segmentation and analysis of the iris. Several morphological operators are developed to segment the iris region from the eye image and also to highlight chosen iris patterns. The extracted features are used to represent and uniquely characterize the iris. In order to properly extract the desired patterns, we also propose an algorithm to produce skeletons with unique paths among end-points. This new representation, obtained by the proposed morphological operators is stored for identification purposes. Results are presented to illustrate the efficiency of the identification system. The proposed system was derived to present low complexity implementation and low storage requirements.

23. Hierarchical Image Analysis Using Irregular Tessellations

A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes.[1] 307E.H. Adelson, E. Simoncelli, and R. Hingorani, "Orthogonal pyramid transforms for image coding," inVisual Communications and Image Processing II, SPIE Proc., vol. 845, pp. 50-58, 1987.

24. IRIS: A Semi-Formal Approach for Detecting Requirements Interactions

Requirements engineering is considered a critical phase of the software development life cycle. However, because of the complexity of today's projects, requirements often have a negative impact on each other. Requirements interaction detection is an important activity for the discovery of such unwanted interactions. Commonly used detection processes are oriented towards the telecommunication domain and are done using either human experts or formal approaches. This paper presents IRIS, which stands for Identifying Requirements Interactions using Semi-formal methods. The novelty of IRIS is threefold: First, IRIS uses semi-formal methods for the detection of interactions between requirements. This helps to fill in the gap between the commonly used informal and formal approaches. Secondly, IRIS is a domain independent approach, which means that it is not limited to the telecommunications domain but can be used in any field. Thirdly, IRIS has a basic core as well as extension hooks for future expansion through the creation of new plug-ins that can be attached to the hooks. This paper first presents an overview of IRIS along with its basic core. It then describes the customizability of IRIS through hooks and plug-ins. Finally it presents the customization of IRIS using different plug-ins for different domains as well as a summary of the results obtained from these domains.

25. Improving iris recognition accuracy via cascaded classifiersZhenan Sun Yunhong Wang Tieniu Tan Jiali Cui Center for Biometrics & Security Res., Chinese Acad. of Sci., Beijing, China;

This paper appears in: Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions onPublication Date: Aug. 2005Volume: 35, Issue: 3On page(s): 435-441

ISSN: 1094-6977INSPEC Accession Number: 8507031Digital Object Identifier: 10.1109/TSMCC.2005.848169Current Version Published: 2005-07-25

AbstractAs a reliable approach to human identification, iris recognition has received increasing attention in recent years. The most distinguishing feature of an iris image comes from the fine spatial changes of the image structure. So iris pattern representation must characterize the local intensity variations in iris signals. However, the measurements from minutiae are easily affected by noise, such as occlusions by eyelids and eyelashes, iris localization error, nonlinear iris deformations, etc. This greatly limits the accuracy of iris recognition systems. In this paper, an elastic iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). In addition, in order to recognize various iris images efficiently a novel cascading scheme is proposed to combine the LFC and an iris blob matcher. When the LFC is uncertain of its decision, poor quality iris images are usually involved in intra-class comparison. Then the iris blob matcher is resorted to determine the input iris' identity because it is capable of recognizing noisy images. Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost.

26.Iris recognition: an emerging biometric technologyWildes, R.P.Proceedings of the IEEEVolume 85, Issue 9, Sep 1997 Page(s):1348 - 1363Digital Object Identifier   10.1109/5.628669Summary:This paper examines automated iris recognition as a biometrically based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment. In particular the biomedical literature suggests 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 machine vision system. The body of this paper details issues in the design and operation of such systems. For the sake of illustration, extant systems are described in some amount of detail

27. Robust Direction Estimation of Gradient Vector Field for Iris RecognitionAs a reliable personal identification method, iris recognition has been receiving increasing attention. Based on the theory of robust statistics, a novel geometry-driven method for iris recognition is presented in this paper. An iris image is considered as a 3D surface of piecewise smooth patches. The direction of the 2D vector, which is the planar projection of the normal vector of image surface, is illumination insensitive and opposite

to the direction of gradient vector. So the directional information of iris image?s gradient vector field (GVF) is used to represent iris pattern. Robust direction estimation, direction diffusion followed by vector directional filtering, is performed on the GVF to extract stable iris feature. Extensive experimental results demonstrate that the recognition performance of the proposed algorithm is comparable with the best method in the open literature.

28. Counterfeit iris detection based on texture analysisZhuoshi Wei Xianchao Qiu Zhenan Sun Tieniu Tan Center for Biometrics & Security Res., Chinese Acad. of Sci., Beijing;

This paper appears in: Pattern Recognition, 2008. ICPR 2008. 19th International Conference onPublication Date: 8-11 Dec. 2008On page(s): 1-4Location: Tampa, FL, ISSN: 1051-4651ISBN: 978-1-4244-2174-9INSPEC Accession Number: 10458017Digital Object Identifier: 10.1109/ICPR.2008.4761673Current Version Published: 2009-01-23

AbstractThis paper addresses the issue of counterfeit iris detection, which is a liveness detection problem in biometrics. Fake iris mentioned here refers to iris wearing color contact lens with textures printed onto them. We propose three measures to detect fake iris: measuring iris edge sharpness, applying Iris-Texton feature for characterizing the visual primitives of iris textures and using selected features based on co-occurrence matrix (CM). Extensive testing is carried out on two datasets containing different types of contact lens with totally 640 fake iris images, which demonstrates that Iris-Texton and CM features are effective and robust in anticounterfeit iris. Detailed comparisons with two state-of-the-art methods are also presented, showing that the proposed iris edge sharpness measure acquires a comparable performance with these two methods, while Iris-Texton and CM features outperform the state-of-the-art.

29. Personal identification based on iris texture analysisLi Ma Tieniu Tan Yunhong Wang Dexin Zhang Inst. of Autom., Chinese Acad. of Sci., Beijing, China;

This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions onPublication Date: Dec. 2003Volume: 25, Issue: 12On page(s): 1519- 1533ISSN: 0162-8828

INSPEC Accession Number: 7858148Digital Object Identifier: 10.1109/TPAMI.2003.1251145Current Version Published: 2003-12-08

AbstractWith an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.