Professor Kevin W. Bowyer's Iris Biometrics Publications

Our research group is part of the Multiple Biometric Grand Challenge and Iris Challenge Evaluation support teams.

Undergrad researcher Sarah Ring is featured in a two-minute video that ran during halftime of the nationally-televised 2007 Notre Dame - Duke football game. Sarah presented a conference paper at BTAS 2008 on her work on the effect of contact lenses on iris biometrics.

Kevin W. Bowyer - Publications related to iris biometrics.

Iris Recognition Using Signal-level Fusion of Frames from Video,
Karen P. Hollingsworth, Tanya Peters, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Information Forensics and Security, to appear.
pdf of this paper.
We take advantage of the temporal continuity in an iris video to improve matching performance using signal-level fusion. From multiple frames of a frontal iris video, we create a single average image. ... No published prior work has shown any advantage of the use of video over still images in iris biometrics.
Using Fragile Bit Coincidence to Improve Iris Recognition,
Karen P. Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
Biometrics: Theory, Applications and Systems (BTAS 09), September 2009, Washington, DC.
pdf of this paper.
... Previous research has shown that iris recognition performance can be improved by making these fragile bits. ... We find that the locations of fragile bits tend to be consistent across different iris codes of the same eye. We present a metrics, called the fragile bit distance, which quantitatively measures the coincidence of the fragile bit patterns in two iris codes. We find that score-fusion of fragile bit distance and Hamming distance works better for recognition than Hamming distance alone. This is the first and only work that we are aware of to use the coincidence of fragile bit locations to improve the accuracy of matches.
Contact Lenses: Handle With Care for Iris Recognition,
Sarah Baker, Amanda Hentz, Kevin W. Bowyer and Patrick J. Flynn,
Biometrics: Theory, Applications and Systems (BTAS 09), September 2009, Washington, DC.
pdf of this paper (not final version).
Many iris recognition systems operate under the assumption that non-cosmetic contact lenses will not affect match quality and the convenience of the system. In this paper we show results opposing this belief ... The false reject rate varies with different types of contacts and the artifacts they produce on iris images.
Stability of the Iris Match Distribution,
Presentation only of work done with Karen Hollingsworth, Sarah Baker, Amanda Hentz, Tanya Peters and Patrick J. Flynn,
Biometrics Consortium Conference (BCC), September 2009, Tampa, FL.
pdf of slides.
The ND-IRIS-0405 Iris Image Dataset,
Kevin W. Bowyer and Patrick J. Flynn,
Notre Dame CVRL Technical Report.
pdf of this report.
The purpose of this document is to describe the content of the ND-IRIS-0405 iris image dataset. This dataset is a superset of the iris image datasets used in ICE 2005 and ICE 2006.
FRVT 2006 and ICE 2006 Large-Scale Experimental Results
P. Jonathon Phillips, W. Todd Scruggs, Alice O'Toole, Patrick J. Flynn, Kevin W. Bowyer, Cathy L. Schott and Matthew Sharpe,
IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.
pdf of this paper.
This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. ...
Empirical Evidence for Correct Iris Match Score Degradation With Increased Time Lapse Between Gallery and Probe Images,
Sarah Baker, Kevin W. Bowyer and Patrick J. Flynn,
International Conference on Biometrics, 1170-1179, June 2009.
pdf of this paper.
We explore the effects of time lapse on iris biometrics using a data set of images with four years time lapse between the earliest and the most recent images of an iris (13 subjecs, 26 irises, 1809 total images. We find that the average fractional distance for a match between two images of an iris taken four years apart is significantly larger than the match for images with only a few months time lapse between them. ... To our knowledge, this is the first and only experimental study of iris match scores under long (multi-year) time lapse.
Overview of the Multiple Biometric Grand Challenge,
P. Jonathon Phillips, Todd Scruggs, Patrick Flynn, Kevin W. Bowyer, Ross Beveridge, Geoff Givens, Bruce Draper and Alice O'Toole,
International Conference on Biometrics, 705-714, June 2009.
pdf of this paper.
The goal of the Multiple Biometric Grand Challenge (MBGC) is to improve the performance of face and iris recognition technology from samples acquired under unconstrained conditions. The MBGC is organized into three challenge problems. Each challenge problem relaxes the constraints in different directions. ...
Image Averaging for Improved Iris Recognition,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
International Conference on Biometrics, 1112-1121, June 2009.
pdf of this paper.
We take advantage of the temporal continuity in an iris video to improve matching performance using signal-level fusion. From multiple frames of an iris video, we create a single average image. Our signal-level fusion method performs better that methods based on single still images, and better than previously published multi-gallery score-fusion methods. ...
The Best Bits in an Iris Code,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (6), 964-973, June 2009.
pdf of this paper.
... The fractional Hamming distance weights all bits in an iris code equally. However, not all the bits in an iris code are equally useful. Our research is the first to present experiments documenting that some bits are more consistent than others. ... The inconsistencies are largely due to the coarse quantization of the phase response. Masking iris code bits corresponding to complex filter responses near the axes of the complex plane improves the separation between the match and nonmatch Hamming distance distributions.
Recent Research Results In Iris Biometrics,
Karen Hollingsworth, Sarah Baker, Sarah Ring, Kevin W. Bowyer and Patrick J. Flynn,
SPIE 7306B: Biometric Technology for Human Identification VI, April 2009. DOI link: http://dx.doi.org/10.1117/12.823095.
pdf of this paper.
... we have collected more than 100,000 iris images for use in iris biometrics research. Using this data, we have developed a number of techniques for improving recognition rates. These techniques include fragile bit masking, signal-level fusion of iris images, and detecting local distortions in iris texture. Additionally, we have shown that large degrees of dilation and long lapses of time between image acquisitions negatively impact performance.
Introduction to the Special Section of Best Papers from the 2007 Biometrics: Theory, Applications and Systems Conference,
Kevin W. Bowyer,
IEEE Transactions on Systems, Man and Cybernetics Pat A, 39 (1), January 2009, 2-3.
pdf of this paper.
... Over 100 papers were submitted to BTAS 07. ... The final result of this process is the set of five papers that appear in this special section. We are particularly fortunate in the way that the five papers in this special section illustrate the breadth of activities in current biometrics research. Face, fingerprint, iris, voice, handwriting, and multimodal biometrics are all represented. ...
Pupil Dilation Degrades Iris Biometric Performance,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
Computer Vision and Image Understanding, 113 (1), January 2009, 150-157.
pdf of this paper.
DOI link to CVIU version of this paper.
... We found that when the degree of dilation is similar at enrollment and recognition, comparisons involving highly dilated pupils result in worse recognition performance than comparisons involving constricted pupils. We also found that when the matched images have similarly highly dilated pupils, the mean Hamming distance of the match distribution increases and the mean Hamming distance of the non-match distribution decreases, bringing the distributions closer together from both directions. We further found that when matching enrollment and recognition images of the same person, larger differences in pupil dilation yield higher template dissimilarities, and so a greater chance of a false non-match. ...
Detection of iris texture distortions by analyzing iris code matching results,
Sarah Ring and Kevin W. Bowyer,
Biometrics: Theory, Applications and Systems, September 2008, Washington, DC.
pdf of this paper.
"Previous work in iris biometrics attempts to cope with occlusion by eyelids / eyelashes and with specular highlights through improved segmentation of the iris region. Our approach assumes that some local distortions of the iris texture are not detected at the segmentation stage, and that these generate corresponding regions of local distortion in the iris code derived from the image. We introduce an approach to detect such regions of local distortion in the iris code through analysis of the iris code matching results. We know of no previous work that attempts to detect distortions of iris texture through analyzing the iris code matching results.
The Iris Challenge Evaluation 2005,
P. Jonathon Phillips, Kevin W. Bowyer and Patrick J. Flynn, Xiaomei Liu and W. Todd Scruggs,
Biometrics: Theory, Applications and Systems, September 2008, Washington, DC.
DOI link to IEEE Xplore version of this paper.
Image Understanding for Iris Biometrics: a Survey,
Kevin W. Bowyer, Karen Hollingsworth and Patrick J. Flynn,
Computer Vision and Image Understanding, 110(2), 281-307, May 2008.
DOI link to CVIU version of this paper.
... Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. ...
Learning to Predict Gender from Irises,
Vince Thomas, Nitesh V. Chawla, Kevin W. Bowyer and Patrick J. Flynn,
IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 07), September 2007.
pdf of this paper.
This paper employs machine learning techniques to develop models that predict gender based on the iris texture features. ...
Guest Editorial: Introduction to the Special Issue on Recent Advances in Biometric Systems,
Kevin W. Bowyer, Venu Govindaraju and Nalini Ratha,
IEEE Transactions on Systems, Man and Cybernetics - B 37 (5), October 2007.
pdf of this paper.
We are pleased to present 14 papers in this special issue devoted to recent advances in biometric systems. ...
Comment on the CASIA version 1.0 Iris Dataset,
P. Jonathon Phillips, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (10), October 2007.
pdf of this paper.
We note that the images in the CASIA version 1.0 iris dataset have been edited so that the pupil area is replaced by a circular region of uniform intensity. We recommend that this dataset is no longer used in iris biometrics research ...
FRVT 2006 and ICE 2006 Large-Scale Results,
P. J. Phillips, W. T. Scruggs, A. J. O'Toole, P. J. Flynn, K.W. Bowyer, C. L. Schott, and M. Sharpe.
National Institute of Standards and Technology, NISTIR 7408, http://face.nist.gov, 2007.
pdf of this report.
This report describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. ...
Experiments With an Improved Iris Segmentation Algorithm,
Xiaomei Liu, Kevin W. Bowyer, and Patrick J. Flynn,
Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID), October 2005, New York, 118-123.
pdf of this paper.
... We have also developed and implemented an improved iris segmentation and eyelid detection stage of the algorithm, and experimentally verified the improvement in recognition performance using the collected dataset. Compared to Masek's original segmentation approach, our improved segmentation algorithm leads to an increase of over 6% in the rank-one recognition rate.