-
The Results of the NICE.II Iris Biometrics Competition,
Kevin W. Bowyer,
Pattern Recognition Letters,
to appear.
pdf of this paper.
... NICE.II focused on performacne in feature extraction and matching.
The eight top-performing algortihsm from NICE.II are considered, and
suggestions are made for lessons that can be drawn from the results.
-
Human and Machine Performance on Periocular Biometrics Under Near-Infrared Light
and Visible Light.
Karen P. Hollingsworth, Shelby S. Darnell, Philip E. Miller, Damon
L. Woodard, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Information Forensics and Security, to appear.
pdf of this paper.
... Previous periocular research has used either visible light or near-infrared light images,
but no prior research has directly compared the two illuminations using images with similar
resolution. We conducted an experiment in which volunteers were asked to compare pairs
of periocular images. ... We calculated performance of three computer algorithms on the
periocular images. Performance for humans and computers was similar.
-
Useful Features for Human Verification in Near-Infrared Periocular Images,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
Image and Vision Computing Journal, to appear.
pdf of this paper.
... We conducted two experiments to determine how humans analyze periocular images. In these
experiments, we presented pairs of images and asked volunteers to determine whether the two
images showed eyes from the same subject or from different subjects. ...
-
Predicting Ethnicity and Gender from Iris Texture,
Stephen Lagree and Kevin W. Bowyer,
IEEE International Conference on Technologies for Homeland Security,
November 2011, Boston, MA.
pdf of this paper.
Previous researchers have reported success in predicting ethnicity and predicting
gender from features of the iris texture. This paper is the first to consider
both problems from similar experimental approaches.
Contributions of this work include greater accuracy than previous work on
predicting ethnicity from iris texture, empirical evidence that suggests that
gender prediction is harder than ethnicity prediction, and
empirical evidence that ethnicity prediction is more difficult for females than
for males.
-
Useful Features for Human Verification in Near-Infrared Periocular Images,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
Image and Vision Computing Journal, to appear.
-
Dilation Aware Multi-Image Enrollment for Iris Biometrics
Estefan Ortiz and Kevin W. Bowyer
International Joint Conference on Biometrics (IJCB 2011), October 2011.
pdf of this paper.
... the probability of a false non-match increases when there is a considerable
variation in pupil size between the enrolled eye image and the probe eye image.
... Our research examines a strategy to improve system performance by implementing
a dilation-aware enrollment phase that chooses eye images based on their respective
empirical dilation ratio distribution.
... Our results show that there is a noticeable improvement over the random scenario
when pupil dilation is accounted for during the enrollment phase.
-
What Surprises Do Identical Twins Have for Identity Science?
Kevin W. Bowyer,
IEEE Computer 44(7), July 2011, 100-102.
pdf of this paper.
Experiments with biometric datasets from identical twins are helping to shape future
research in face and iris recognition.
-
Genetically Identical Irises Have Texture Similarity That Is
Not Detected By Iris Biometrics,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
Computer Vision and Image Understanding 115, 1493-1502, 2011.
pdf of this paper.
As the standard iris biometric algorithm "sees" them, the left and right irises
of the same person are as different as irises of unrelated people. Similarly,
in terms of iris biometric matching, the eyes of identical twins are as different
as irises of unrelated people.
The left and right eyes of an individual or the eyes of identical twins are
examples of genetically identical irises.
In experiments with human observers viewing pairs of iris images acquired using an
iris biometric system, we have found that there is recognizable similarity in
the left and right irises of an individual and the irises of identical twins.
This result suggests that iris texture analysis different from that performance in the
standard iris biometric algorithm may be able to answer questions that iris biometrics
cannot answer.
-
Improved Iris Recognition Through Fusion of Hamming Distance and Fragile Bit Distance,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn.
IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
pdf of this paper.
... We find that the locations of fragile bits tend to be consistent across
different iris codes of the same eye.
We present a metric, called the fragile bit distance, which quantitatively
measures the coincidence of the fragle bit patterns in two iris codes.
We find that score fusion of fragile bit distances and Hamming distance works better
for recognition than Hamming distance alone.
To our knowledge, this is the first and only work to use the
coincidence of fragile bit locations to improve the accuracy of matches.
-
A Cross-Sensor Evaluation of Three Commercial Iris Cameras for Iris Biometrics,
Ryan Connaughton, Amanda Sgroi, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Computer Society Workshop on Biometrics, June 2011.
pdf of this paper.
... This work presents experiments which compare three commercially available iris
sensors and investigates the impact of cross-sensor matching on system
performance. ...
-
Experimental evidence of a template aging effect in iris biometrics,
Sam Fenker and Kevin W. Bowyer,
IEEE Computer Society Workshop on Applications of Computer Vision, January 2011.
pdf of this paper.
Baker et al recently presented the first published evidence of a
template aging effect, using images acquired from 2004 through 2008
with an LG 2200 iris imaging system,
representing a total of 13 subjects (26 irises).
We report on a template aging study involving two different iris
recognition algorithms, a larger number of subjects (43),
a more modern imaging system (LG 4000), and over a
shorter time-lapse (2 years). We also investigate the
degree to which the template aging effect may be related
to pupil dilation and/or contact lenses.
-
Identifying useful features for recognition in near-infrared periocular images
Karen Hollingsworth, Kevin W. Bowyer and Patrick J Flynn,
Biometrics Theory, Applications and Systems (BTAS).
pdf of this paper.
... We presented pairs of periocular images to testers and asked them
to determine whether the two images were from the same person or
from different people. Our testers correctly determined the relationship
in over 90% of the queries. We asked them to describe what features in
the images were helpful to them in making their decisions. We found that
eyelashes, tear ducts, shape of the eye, and eyelids were used most frequently
in determining whether two images were of the same person. ...
-
Human perceptual categorization of iris texture patterns,
Louise Stark, Kevin W. Bowyer and Stephen Siena,
Biometrics Theory, Applications and Systems (BTAS).
pdf of this paper.
We report on an experiment in which observers were asked to browse a set of
100 iris images and group them into categories based on similarity of
overall texture appearance. Results indicate that there is a natural
categorization of iris images into a small number of high-level
categories, and then also into sub-categories. ...
-
Degradation of iris recognition performance due to non-cosmetic
prescription contact lenses,
Sarah Baker, Amanda Hentz, Kevin W. Bowyer and Patrick J. Flynn,
Computer Vision and Image Understanding 114 (9), 1030-1044, September 2010.
pdf of this paper.
Many iris recognition systems operate under the assumption that non-cosmetic contact
lenses have no or minimal effect on iris biometrics performance. ... This is the
first study to document degraded iris biometrics performance with non-cosmetic contact
lenses.
-
Human Versus Biometric Perception of Iris Texture,
Presentation only of work done with Karen Hollingsworth,
Steve Lagree, Sam Fenker and Patrick J. Flynn,
Biometrics Consortium Conference (BCC), September 2010,
Tampa, FL.
pdf of slides.
-
Introduction to the Special Issue on Recent Advances In Biometrics,
Kevin W. Bowyer,
IEEE Transactions on Systems, Man and Cybernetics - Part A, 40 (3), May 2010, 434-436.
pdf of this paper.
As with BTAS 07, a biometrics-themed special issue of Systems Man and Cybernetics - Part A was
organized following BTAS 08.
However, unlike the SMC-A special section drawn from BTAS 07, this special issue had an "open"
call for papers, meaning that submissions were not limited to papers presented at BTAS 08. ...
A total of 31 submissions were received for the special issue. ...
The ten papers that appear in this special issue represent the result of this process.
Four of the ten papers appearing in this special issue are revised and extended versions of
papers presented in the closing session of the BTAS 08 conference, a session which, by BTAS tradition,
is reserved for the submissions that receive the overall best reviews from the conference program committee.
The papers in this special issue cover a range of different biometric modalities, including face, ear,
iris, signature and multi-modal. In the area of face recognition, there are papers dealing with 2D, 3D, and
hand-drawn sketches. Also, the papers in this special issue range from relatively application oriented to
relatively theoretical. One common theme is that each paper addresses an important current topic in
biometrics research and makes a novel contribution to the state of the art.
-
Factors That Degrade the Match Distribution In Iris Biometrics,
Kevin W. Bowyer, Sarah E. Baker, Amanda Hentz, Karen Hollingsworth, Tanya Peters and Patrick J. Flynn,
Identity in the Information Society, 2 (3) 327-343, December 2009.
DOI link. (open access)
We consider three "accepted truths" about iris biometrics, involving pupil dilation,
contact lenses and template aging. We also consider a relatively ignored issue that
may arise in system interoperability. Experimental results from our laboratory
demonstrate that the three accepted truths are not entirely true, and also that
interoperability can involve subtle performance degradation. All four of these problems
affect primarily the stability of the match, or authentic, distribution of template
comparison scores rather than the non-match, or imposter, distribution of scores.
In this sense, these results confirm the security of iris biometrics in an identity
verification scenario. We consider how these problems affect the usability and
security of iris biometrics in large-scale applications, and suggest possible remedies.
-
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
4 (4), 837-848, December 2009.
pdf of this paper.
DOI link.
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.
-
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 Computer Vision Research Lab at the University of Notre Dame began
collecting iris images in the spring semester of 2004. The initial data
collections used an LG 2200 iris imaging system for image acquisition.
Image datasets acquired in 2004-2005 at Notre Dame with this LG 2200
have been used in the ICE 2005 and ICE 2006 iris biometric evaluations.
The ICE 2005 iris image dataset has been distributed to over 100 research groups
around the world.
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 32 (5), May 2010, 831-846.
pdf of this paper.
DOI link.
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, June 2009, 1170-1179.
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 subjects, 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, June 2009, 705-714.
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, June 2009, 1112-1121.
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. ...
-
Towards the Next Generation of Iris Biometrics Science,
Kevin W. Bowyer, Patrick J. Flynn, Karen Hollingsworth, Sarah Baker and Sarah Ring.
SPIE Newsroom,
2009. DOI link: http://dx.doi.org/10.1117/2.1200904.1595.
pdf of this paper.
(A SPIE Newsroom article that gives an overview of the paper and invited talk.)
-
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.
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 - Part A, 39 (1), January 2009, 2-3.
pdf of this paper.
DOI link.
... 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.
... 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. ...
Our research group is part of the
Multiple Biometric Grand Challenge and
Iris Challenge Evaluation support teams.
-
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.
Our research group is part of the
Multiple Biometric Grand Challenge and
Iris Challenge Evaluation support teams.
-
Detection of Iris Texture Distortions By Analyzing Iris Code
Matching Results,
Sarah Ring and Kevin W. Bowyer,
Biometrics: Theory, Applications and Systems (BTAS 08), 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 (BTAS 08), September 2008,
Washington, DC.
DOI link.
-
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.
... 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. ...
Our research group is part of the
Multiple Biometric Grand Challenge and
Iris Challenge Evaluation support teams.
-
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. ...
Our research group is part of the
Multiple Biometric Grand Challenge and
Iris Challenge Evaluation support teams.
-
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.
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