509 research outputs found
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
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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
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
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
Privacy-Preserving Eye Videos using Rubber Sheet Model
Video-based eye trackers estimate gaze based on eye images/videos. As
security and privacy concerns loom over technological advancements, tackling
such challenges is crucial. We present a new approach to handle privacy issues
in eye videos by replacing the current identifiable iris texture with a
different iris template in the video capture pipeline based on the Rubber Sheet
Model. We extend to image blending and median-value representations to
demonstrate that videos can be manipulated without significantly degrading
segmentation and pupil detection accuracy.Comment: Will be published in ETRA 20 Short Papers, June 2-5, 2020, Stuttgart,
Germany Copyright 2020 Association for Computing Machiner
On the vulnerability of iris-based systems to a software attack based on a genetic algorithm
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33275-3_14Proceedings of 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, ArgentinaThe vulnerabilities of a standard iris verification system to a novel indirect attack based on a binary genetic algorithm are studied. The experiments are carried out on the iris subcorpus of the publicly available BioSecure DB. The attack has shown a remarkable performance, thus proving the lack of robustness of the tested system to this type of threat. Furthermore, the consistency of the bits of the iris code is analysed, and a second working scenario discarding the fragile bits is then tested as a possible countermeasure against the proposed attack.This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM, Bio-Challenge (TEC2009-11186) from Spanish MICINN, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica
TOPS: an internet-based system to prevent healthy subjects from over-volunteering for clinical trials
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