Iris recognition as a biometric method after cataract surgery

BACKGROUND: Biometric methods are security technologies, which use human characteristics for personal identification. Iris recognition systems use iris textures as unique identifiers. This paper presents an analysis of the verification of iris identities after intra-ocular procedures, when individuals were enrolled before the surgery. METHODS: Fifty-five eyes from fifty-five patients had their irises enrolled before a cataract surgery was performed. They had their irises verified three times before and three times after the procedure, and the Hamming (mathematical) distance of each identification trial was determined, in a controlled ideal biometric environment. The mathematical difference between the iris code before and after the surgery was also compared to a subjective evaluation of the iris anatomy alteration by an experienced surgeon. RESULTS: A correlation between visible subjective iris texture alteration and mathematical difference was verified. We found only six cases in which the eye was no more recognizable, but these eyes were later reenrolled. The main anatomical changes that were found in the new impostor eyes are described. CONCLUSIONS: Cataract surgeries change iris textures in such a way that iris recognition systems, which perform mathematical comparisons of textural biometric features, are able to detect these changes and sometimes even discard a pre-enrolled iris considering it an impostor. In our study, re-enrollment proved to be a feasible procedure

Information theory and the iriscode

Iris recognition has legendary resistance to False Matches, and the tools of information theory can help to explain why. The concept of entropy is fundamental to understanding biometric collision avoidance. This paper analyses the bit sequences of IrisCodes computed both from real iris images and from synthetic “white noise” iris images whose pixel values are random and uncorrelated. The capacity of the IrisCode as a channel is found to be 0.566 bits per bit encoded, of which 0.469 bits of entropy per bit is encoded from natural iris images. The difference between these two rates reflects the existence of anatomical correlations within a natural iris, and the remaining gap from one full bit of entropy per bit encoded reflects the correlations in both phase and amplitude introduced by the Gabor wavelets underlying the IrisCode. A simple two-state Hidden Markov Model is shown to emulate exactly the statistics of bit sequences generated both from natural and white noise iris images, including their “imposter” distributions, and may be useful for generating large synthetic IrisCode databases.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TIFS.2015.250019

Iris classification based on sparse representations using on-line dictionary learning for large-scale de-duplication applications

De-duplication of biometrics is not scalable when the number of people to be enrolled into the biometric system runs into billions, while creating a unique identity for every person. In this paper, we propose an iris classification based on sparse representation of log-gabor wavelet features using on-line dictionary learning (ODL) for large-scale de-duplication applications. Three different iris classes based on iris fiber structures, namely, stream, flower, jewel and shaker, are used for faster retrieval of identities. Also, an iris adjudication process is illustrated by comparing the matched iris-pair images side-by-side to make the decision on the identification score using color coding. Iris classification and adjudication are included in iris de-duplication architecture to speed-up the identification process and to reduce the identification errors. The efficacy of the proposed classification approach is demonstrated on the standard iris database, UPOL

Searching for doppelgängers: Assessing the universality of the IrisCode impostors distribution

© The Institution of Engineering and Technology 2016. The authors generated 316,250 entire distributions of IrisCode impostor scores, each distribution obtained by comparing one iris against hundreds of thousands of others in a database including persons spanning 152 nationalities. Altogether 100 billion iris comparisons were performed in this study. The purpose was to evaluate whether, in the tradition of Doddington's Zoo, some individuals are inherently more prone than most to generate iris false matches, while others are inherently less prone. With the standard score normalisation disabled, a detailed inter-quantile analysis showed that meaningful deviations from a universal impostors distribution occur only for individual distributions that are highly extreme in both their mean and their standard deviation, and which appear to make up <1% of the population. In general, when different persons are compared, the IrisCode produces relatively constant dissimilarity distances having an invariant narrow distribution, thanks to the large entropy which lies at the heart of this biometric modality. The authors discuss the implications of these findings and their caveats for various search strategies, including '1-to-first' and '1-to-many' iris matching

Broken Symmetries, Random Morphogenesis, and Biometric Distance

This paper discusses the role of symmetry-breaking in biometric recognition. Using publicly available databases, we investigate three kinds of broken symmetries in iris patterns: binocular, monocular, and monozygotic. We report a small but statistically significant difference in similarities between the ipsilateral and the contralateral eyes of twins, and also between genetically identical and nonidentical eyes. Another new finding is a doubling in the variance of Hamming distance scores under a simple monocular mirror transformation, which is consistent with an assessment of entropy

Multivariate texture discrimination based on geodesics to class centroids on a generalized Gaussian Manifold

A texture discrimination scheme is proposed wherein probability distributions are deployed on a probabilistic manifold for modeling the wavelet statistics of images. We consider the Rao geodesic distance (GD) to the class centroid for texture discrimination in various classification experiments. We compare the performance of GD to class centroid with the Euclidean distance in a similar context, both in terms of accuracy and computational complexity. Also, we compare our proposed classification scheme with the k-nearest neighbor algorithm. Univariate and multivariate Gaussian and Laplace distributions, as well as generalized Gaussian distributions with variable shape parameter are each evaluated as a statistical model for the wavelet coefficients. The GD to the centroid outperforms the Euclidean distance and yields superior discrimination compared to the k-nearest neighbor approach

Designing and Implementing an Information Literacy Course in the Humanities

As instructors in the Z. Smith Reynolds Library information literacy program at Wake Forest University, we are expanding beyond our introductory course model to teach discipline-specific information literacy courses. Z. Smith Reynolds Library initiated an information literacy program in 2002 and currently offers a 1-credit elective, taught in 15 sections per semester. Advanced discipline-specific courses were added in Spring 2008, and include courses in the social sciences, business and economics, and the sciences. As the subject specialists for art, dance, literature, music, religion and theatre, we were charged with creating a credit-bearing arts and humanities information literacy course, LIB250: Humanities Research Sources and Strategies. In addition to our arts and humanities course content and methodologies, we incorporated web2.0 technologies throughout course design and delivery in order to streamline planning and to facilitate student engagement. In our course preparation, we utilized Google Docs for collaborative brainstorming, planning, organization and self-evaluation. Our students used Google Docs for submitting their course assignments, which included a faculty or practitioner interview and the final project for the course. The project was an annotated bibliography that extended beyond books and journal articles to include additional elements such as scholarly associations, core journals, primary sources, and major special collections related to their topics. A blog was used for the course syllabus, incorporating assignment information and supplementary resources for class topics; student blogging included reflection and feedback on each lecture as well as application of class content to their research topics. A visit to the Library\u27s Rare Books Reading Room exposed the students to the Library\u27s unique holdings and introduced them to rare and archival resources at other libraries. Our article presents this process from start to finish, including the steps we went through to plan the course, the utilization of web2.0 applications, the course syllabus and assignments, marketing efforts, as well as the course implementation in Spring 2009 and 2010, and what we and our students learned from this process

Hardware-software co-design of an iris recognition algorithm

This paper describes the implementation of an iris recognition algorithm based on hardware-software co-design. The system architecture consists of a general-purpose 32- bit microprocessor and several slave coprocessors that accelerate the most intensive calculations. The whole iris recognition algorithm has been implemented on a low-cost Spartan 3 FPGA, achieving significant reduction in execution time when compared to a conventional software-based application. Experimental results show that with a clock speed of 40 MHz, an IrisCode is obtained in less than 523 ms from an image of 640x480 pixels, which is just 20% of the total time needed by a software solution running on the same microprocessor embedded in the architecture.Peer ReviewedPreprin

MinMax Radon Barcodes for Medical Image Retrieval

Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US

Ethnicity and Biometric Uniqueness: Iris Pattern Individuality in a West African Database

We conducted more than 1.3 million comparisons of iris patterns encoded from images collected at two Nigerian universities, which constitute the newly available African Human Iris (AFHIRIS) database. The purpose was to discover whether ethnic differences in iris structure and appearance such as the textural feature size, as contrasted with an all-Chinese image database or an American database in which only 1.53% were of African-American heritage, made a material difference for iris discrimination. We measured a reduction in entropy for the AFHIRIS database due to the coarser iris features created by the thick anterior layer of melanocytes, and we found stochastic parameters that accurately model the relevant empirical distributions. Quantile-Quantile analysis revealed that a very small change in operational decision thresholds for the African database would compensate for the reduced entropy and generate the same performance in terms of resistance to False Matches. We conclude that despite demographic difference, individuality can be robustly discerned by comparison of iris patterns in this West African population.Comment: 8 pages, 8 Figure