-
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 algorithsm from NICE.II are considered, and
suggestions are made for lessons that can be drawn from the results.
-
A Sparse Representation Approach to Face Matching Across Plastic Surgery,
Gaurav Aggarwal, Soma Biswas, Patrick J. Flynn and Kevin W. Bowyer,
IEEE Workshop on Applications of Computer Vision,
January 2012, Colorado Springs, CO.
pdf of this paper.
... In this paper, we propose a novel approach to address the challenges involved
in automatic matching of faces across plastic surgery variations. ....
Extensive experiments conducted on a recently introduced plastic surgery database
consisting of 900 subjects highlight the effectiveness of the proposed approach.
-
Predicting Good, Bad and Ugly Match Pairs,
Gaurav Aggarwal, Soma Biswas, Patrick J. Flynn and Kevin W. Bowyer,
IEEE Workshop on Applications of Computer Vision,
January 2012, Colorado Springs, CO.
pdf of this paper.
... The recently introduced GBU challenge problems indicates that even when the
impact of most known factors is eliminated or significantly reduced by the data
collection and experimental protocol, there can be significant variation in
performance across different partitions of the data. ... In this paper, we
investigate various image and facial characteristics that can account for the observed
and significant difference in performance across these partitions. ...
-
Color Balancing for Change Detection in Multitemporal Images,
Jim Thomas, Kevin W. Bowyer and Ahsan Kareem,
IEEE Workshop on Applications of Computer Vision,
January 2012, Colorado Springs, CO.
pdf of this paper.
... In this paper we address color balancing for the purpose of change detection.
... We evaluated the proposed method against other state-of-the-art ones using a
database consisting of aerial image pairs. The test image pairs were taken at
different times, under different lighting conditions, and with different scene
geometries and camera positions. On this database, our proposed approach
outperformed other state-of-the-art algorithms.
-
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.
-
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. ...
Reprinted in the Journal of Intelligence Community Research and Development.
-
Multidimensional Scaling for Matching Low-resolution Face Images,
Soma Biswas, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
pdf of this paper.
... we propose a novel approach for matching low resolution probe images
with higher resolution gallery images ...
The proposed method simultaneously embeds the low resolution probe images
and the high resolution gallery images in a common space such that the
distances between them in the transformed space approximates the
distances had both the images been of high resolution.
The two mappings are learned simultaneously from high resolution training
images using iterative majorization algorithm.
Extensive evaluation of the proposed approach on the Multi-PIE dataset with
probe image resolution as low as 8 x 6 pixels illustrates the usefulness
of the method. We show that the proposed approach improves the matching
performance significantly as compared to performing matching in the
low-resolution domain or using super-resolution techniques to obtain a
higher-resolution test image prior to recognition. ...
-
A Study of Face Recognition of Identical Twins By Humans,
Soma Biswas, Kevin W. Bowyer and Patrick J. Flynn,
International Workshop on Information Forensics and Security (WIFS 2011), December 2011
Foz do Iguacu, Brazil.
pdf of this paper.
In this work, we investigate human capability to distinguish between identical
twins. If humans are able to distinguish between facial images of identical twins,
it would suggest that humans are capable of identifying discriminating facial
traits that can potentially be useful to develop algorithms for this very
challenging problem. Experiments with different viewing times and imaging
conditions are conducted to determine if humans viewing a pair of facial images can
perceive if the image pairs belong to the same person or to a pair of identical
twins. The experiments are conducted on 186 twin subjects, making it the largest such
study in the literature to date.
-
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.
-
Twins 3D Face Recognition Challenge,
Vipin Vijayan, Kevin W. Bowyer, Patrick Flynn, Di Huang, Liming Chen, Mark Hansen,
Omar Ocegueda, Shishir Shah, Ioannis Kakadiaris,
International Joint Conference on Biometrics (IJCB 2011), October 2011.
pdf of this paper.
Existing 3D face recognition algorithms have achieved high enough
performances against public datasets like FRGC v2, that it is
difficult to achieve further significant increases in recognition
performance. However, the 3D TEC dataset is a more challenging
dataset which consists of 3D scans of 107 pairs of twins that were
acquired in a single session, with each subject having a scan of
a neutral expression and a smiling expression. The combination
of factors related to the facial similarity of identical
twins and the variation in facial expression makes this a
challenging dataset. We conduct experiments using state of the art
face recognition algorithms and present the results.
Our results indicate that 3D face recognition of identical twins
in the presence of varying facial expressions is far from a
solved problem, but that good performance is possible.
-
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 fragile 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. ...
-
Towards a robust automated hurricane damage assessment from high-resolution images,
James Thomas, Ahsan Kareem, and Kevin W. Bowyer,
13th International Conference on Wind Engineering (ICWE 13), July 2011.
pdf of this paper.
In the event of a natural disaster such as hurricane or earthquake, estimating the extent of
damage is necessary for implementing fast and effective recovery measures. Images of affected
areas are easily obtained through satellite or aerial sensors. The key objects of interest
in such images are buildings, as damage directly impacts lives. This work aims to build a system
capable of fine-grained damage analysis by comparing before and after storm images. ...
-
Fusion of Face and Iris Biometrics from a Stand-Off Video Sensor,
Ryan Connaughton, Kevin W. Bowyer and Patrick J. Flynn,
22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2011),
April 2011, Cincinnati, Ohio.
-
Distinguishing Identical Twins By Face Recognition,
P. Jonathon Phillips, Patrick J. Flynn, Kevin W. Bowyer, Richard W. Vorder Bruegge,
Patrick J. Grother, George W. Quinn, Matthew Pruitt,
IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011),
March 2011, 185-192.
pdf of this paper.
This paper measures ability of face recognition algorithms to distinguish between identical
twin siblings. The experimental dataset consists of images taken of 126 pairs of identical
twins (252 people) collected on the same day and 24 pairs of identical twins (48 people)
with images collected one year apart. Recognition experiments are conducted using three
of the top submissions to the Multiple Biometric Evaluation (MBE) 2010 Still Face Track. ...
-
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.
-
Detecting Questionable Observers Using Face Track Clustering,
Jeremiah Barr, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Computer Society Workshop on Applications of Computer Vision, January 2011.
pdf of this paper.
We introduce the questionable observer detection problem:
Given a collection of videos of crowds, determine
which individuals appear unusually often across the set of
videos. The algorithm proposed here detects these individuals
by clustering sequences of face images. ...
-
Detecting and Ordering Salient Regions,
Larry Shoemaker, Robert Banfield, Lawrence O. Hall, Kevin W. Bowyer and
W. Philip Kegelmeyer,
Data Mining and Knowledge Discovery 12 (1-2), January 2011, 259-290.
http://dx.doi.org/10.1007/s10618-010-0194-6
pdf of this paper.
We describe an ensemble approach to learning salient regions from
arbitrarily partitioned data. ... We combine a fast ensemble learning
algorithm with scaled probabilistic majority voting in order to learn an
accurate classifier ...
-
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 10), 2010.
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. ...
-
Efficacy of Damage Detection Measures from Aerial Images,
Jim Thomas, Ahsan Kareem and Kevin W. Bowyer,
11-th Americas Conference on Wind Engineering, June 2009.
pdf of this paper.
Estimating the extent of damage caused by natural disasters is necessary for
implementing effective recovery measures. Aerial images of affected areas are
easily obtained through satellite or aerial sensors. A careful analysis of
images from before and after an event facilitates rapid detection and assessment
of damage. Significant previous research has been done on developing measures to
quantify the damage. In this study we evaluate the efficacy of existing change
measures used to estimate damage. Determining the efficacy of these damage measures
in definitive characterization of damage states is necessary for accurate automated
assessment of windstorm damage.
-
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.
-
Using Multi-Instance Enrollment to
Improve Performance of 3D Face Recognition,
Timothy C. Faltemier, Kevin W. Bowyer and Patrick J. Flynn,
Computer Vision and Image Understanding 112 (2), November 2008, 114-125.
Preprint pdf version of this paper.
DOI link.
This paper explores the use of multi-instance enrollment as a means to
improve the performance of 3D face recognition. Experiments are
performed using the ND-2006 3D face data set which contains 13,450
scans of 888 subjects. This is the largest 3D face data set currently
available and contains a substantial amount of varied facial
expression. Results indicate that the multi-instance enrollment
outperforms a state-of-the-art component-based recognition approach ...
-
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.
-
Multi-factor Approach To Improving Recognition Performance In
Surveillance-quality Video,
Deborah Thomas, Kevin W. Bowyer and Patrick J. Flynn,
Biometrics: Theory, Applications and Systems (BTAS 08), September 2008,
Washington, DC.
DOI link.
-
Profile Face Detection: A Subset Multi-biometric Approach,
James Gentile, Kevin W. Bowyer and Patrick J. Flynn,
Biometrics: Theory, Applications and Systems (BTAS 08), September 2008,
Washington, DC.
DOI link.
-
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.
-
A Region Ensemble for 3D Face Recognition,
Timothy Faltemier, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Information Forensics and Security,
3(1):62-73, March 2008.
DOI link.
... we introduce a new system for 3D face recognition based on the fusion
of results from a committee of regions that have been independently matched.
... Rank-one recognition rates of 97.2% and verification rates of 93.2% at
0.1% false accept rate are reported and compared to other methods published
on the Face Recognition Grand Challenge v2 data set.
-
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.
-
Using Classifier Ensembles to Label Spatially Disjoint Data,
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall,
Kevin W. Bowyer and W. Philip Kegelmeyer,
Information Fusion
9(1), 120-133, January 2008.
pdf of this paper.
We describe an ensemble approach to learning from
arbitrarily partitioned data. ...
We combine a fast ensemble learning algorithm with
probabilistic majority voting in order to learn an
accurate classifier from such data. ...
-
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.
-
A Fast Algorithm for ICP-based 3D Shape Biometrics,
Ping Yan and Kevin W. Bowyer,
Computer Vision and Image Understanding,
107 (3), 195-202, September 2007.
pdf of this paper.
... we present a novel approach, called "Pre-computed Voxel
Nearest Neighbor," to reduce the computational time for
shape matching in a biometrics context. The approach shifts
the heavy computation burden to the enrollment stage, which
is done offline. Experiments in 3D ear biometrics with
369 subjects and 3D face biometrics with 219 subjects
demonstrate the effectiveness of our approach.
-
Biometric Recognition Using Three-dimensional Ear Shape,
Ping Yan and Kevin W. Bowyer.
IEEE Transactions on Pattern Analysis and
Machine Intelligence 29 (8), 1297-1308, August 2007.
pdf of this paper.
... We present a complete system for ear biometrics,
including automated segmentation of the ear in a
profile view image and 3D shape matching for recognition.
We evaluated this system with the largest experimental
study to date in ear biometrics, achieving
a rank-one recognition rate of 97.8% for an identification
scenario, and equal error rate of 1.2% for a
verification scenario on a database of 415 subjects and
1,386 total probes.
-
Actively Exploring Face Space(s) for Improved Face Recognition,
Nitesh V. Chawla and Kevin W. Bowyer,
AAAI 2007, Vancouver, July 2007.
pdf of this paper.
We propose a learning framework that actively explores creation
of face space(s) by selecting images that are complementary
to the images already represented in the face space.
We also construct ensembles of classifiers learned from such
actively sampled image sets, which further provides improvement
in the recognition rates. ...
-
Boosting Lite - Handling Larger Datasets and Slower Base
Classifiers,
Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer
and W. Philip Kegelmeyer,
Multiple Classifier Systems (MCS) 2007,
Prague, May 2007.
pdf of this paper.
... we examine ensemble algorithms (Boosting Lite and Ivoting)
that provide accuracy approximating a single classifier, but
which require significantly fewer training examples. ...
-
A Comparison of Decision Tree Ensemble Creation Techniques,
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, and W. Philip Kegelmeyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
29 (1), 173-180, January 2007.
pdf of this paper.
appendix to the paper.
We experimentally evaluate bagging and seven other randomization-based approaches to
creating an ensemble of decision tree classifiers. Statistical tests were performed
on experimental results from 57 publicly available data sets. ...
-
Face Recognition Using 2D, 3D and Infra-Red:
Is Multi-modal Better than Multi-sample?
Kevin W. Bowyer, Kyong I. Chang, Patrick J. Flynn and Xin Chen,
Proceedings of the IEEE,
94 (11), 2000-2012, November 2006.
pdf of this paper.
We compare the performance improvement
obtained by combining three-dimensional or infra-red
with normal intensity images (a multi-modal approach)
to the performance improvement obtained by using
multiple intensity images (a multi-sample approach).
Combining results from different types of imagery
gives significantly higher recognition rates than
are obtained by using a single intensity image. However,
significantly higher recognition rates are also
obtained by combining results from multiple intensity images.
-
Multiple Nose Region Matching for 3D Face Recognition
Under Varying Facial Expression,
Kyong I. Chang, Kevin W. Bowyer, and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence,
28 (10), 1695-1700, October 2006.
pdf of this paper.
An algorithm is proposed for 3D face recognition in the
presence of varied facial expressions. It is based on combining
the match scores from matching multiple overlapping regions
around the nose. Experimental results are presented using the
largest database employed to date in 3D face recognition
studies, over 4,000 scans of 449 subjects. ...
-
Multi-modal Biometrics: An Overview,
Kevin W. Bowyer, et al,
Second Workshop on Multi-Modal User Authentication (MMUA 2006),
May 2006, Toulouse, France.
pdf of this paper.
The topic of multi-modal biometrics has attracted
strong interest in recent years. This paper categorizes
approaches to multi-modal biometrics based on the
biometric source, the type of sensing used, and the
depth of collaborative interaction in the processing.
This paper also attempts to identify some of the
challenges and issues that confront research in multi-modal
biometrics.
This paper represents the invited talk given to open the first day of the workshop.
-
A Survey of Approaches and Challenges in 3D and
Multi-modal 3D+2D Face Recognition,
Kevin W. Bowyer, Kyong Chang, and Patrick J. Flynn,
Computer Vision and Image Understanding
101 (1), January 2006, 1-15.
pdf of this paper.
... This survey focuses on face recognition performed by
matching models of the three-dimensional shape of the face,
either alone or in combination with matching corresponding
two-dimensional intensity images.
This article was number one on the
CVIU "Top 25" list for
the quarters October - December 2005 and
January - March of 2006, and in the top ten for seven
consecutive quarters.
-
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.
-
Eye Perturbation Approach for Robust Recognition of Inaccurately
Aligned Faces,
Jaesik Min, Kevin W. Bowyer, and Patrick J. Flynn,
Fifth International Conference on Audio- and Video-Based Biometric
Person Authentication (AVBPA 2005),
July 2005, New York, 41-50.
pdf of this paper.
... For improved performance and robustness to the eye location variation,
we propose an eye perturbation approach that generates multiple face
extractions from a query image by using the perturbed eye locations
centered at the initial eye locations. ...
-
Infra-Red and Visible-Light Face Recognition,
Xin Chen, Patrick J. Flynn, and Kevin W. Bowyer,
Computer Vision and Image Understanding
99 (3), September 2005, 332-358.
pdf of this paper.
... We find that in a scenario
involving time lapse between gallery and probe,
and relatively controlled lighting,
(1) PCA-based recognition using visible images outperforms
PCA-based recognition using infra-red images, and (2) the combination
of PCA-based recognition using visible and infra-red imagery
substantially outperforms either one individually...
This article was number two on the
CVIU "Top 25" list for
July - September of 2005.
-
Ensembles of Classifiers from Spatially Disjoint Data,
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer,
and W. Philip Kegelmeyer,
Springer-Verlag LNCS 3541: 6th International Workshop
on Multiple Classifier Systems (MCS 2005),
Monterey, CA, June 2005, 196-205.
pdf of this paper.
... We describe an ensemble learning approach that
accurately learns
from data which has been partitioned according to the
arbitrary spatial
requirements of a large-scale simulation wherein
classifiers may be trained only
the data local to a given partition. As a result,
the class statistics can vary from
partition to partition; some classes may even be missing
from some partitions.
-
Empirical Evaluation of Advanced Ear Biometrics,
Ping Yan and Kevin W. Bowyer,
Workshop on Empirical Evaluation Methods in Computer Vision,
San Diego, CA, June 2005.
pdf of this paper.
We present results of the largest experimental investigation of
ear biometrics to date. Approaches considered include a PCA
("eigen-ear") approach with 2D intensity images, achieving 63.8%
rank-one recognition; a PCA approach with range images, achieving
55.3% Hausdorff matching of edge images from range images,
achieving 67.5% and ICP matching of the 3D data, achieving 98.7%.
ICP based matching not only achieves the best performance, but also
shows good scalability with size of dataset. The data set used
represents over 300 persons, each with images acquired on at least
two different dates. In addition, the ICP-based approach is further
applied on an expanded data set of 404 subjects, and achieves 97.5%
rank one recognition rate. In order to test the robustness and
variability of ear biometrics, ear symmetry is also investigated.
In our experiments around 90% of peoples' right and left ears
are symmetric.
-
Overview of the Face Recognition Grand Challenge,
P. Jonathon Phillips, Patrick J. Flynn, Todd Scruggs,
Kevin W. Bowyer, Jin Chang, Kevin Hoffman, Joe Marques,
Jaesik Min, and William Worek,
Computer Vision and Pattern Recognition (CVPR 2005),
San Diego, June 2005, I:947-954.
pdf of this paper.
Our research group is part of the
Face Recognition Grand Challenge support team.
-
Random Subspaces and Subsampling for 2-D Face Recognition,
Nitesh V. Chawla and Kevin W. Bowyer,
Computer Vision and Pattern Recognition (CVPR 2005) ,
San Diego, June 2005, II: 582-589.
pdf of this paper.
Random subspaces are a popular ensemble construction technique that
improves the accuracy of weak classifiers. It has been shown, in
different domains, that random subspaces combined with weak classifiers
such as decision trees and nearest neighbor classifiers can provide an
improvement in accuracy. In this paper, we apply the random subspace
methodology to the 2-D face recognition task. The main goal of the paper
is to see if the random subspace methodology can do as well, if not
better, than the single classifier constructed on the tuned face space.
We also propose the use of a validation set for tuning the face space,
to avoid bias in the accuracy estimation. In addition, we also compare the
random subspace methodology to an ensemble of subsamples of image data.
This work shows that a random subspaces ensemble can outperform a well-tuned
single classifier for a typical 2-D face recognition problem. The random
subspaces approach has the added advantage of requiring less careful tweaking.
-
An Evaluation of Multi-modal 2D+3D Face Biometrics,
Kyong I. Chang, Kevin W. Bowyer, and Patrick J. Flynn,
IEEE Transactions on Pattern Analysis and Machine Intelligence
27 (4), April 2005, 619-624.
pdf of this paper.
We report on the largest experimental study to date in multi-modal
2D+3D face recognition ... Major conclusions are: (1) 2D and 3D have
similar recognition performance when considered individually,
(2) Combining 2D and 3D results using a simple weighting scheme
outperforms either 2D or 3D alone, (3) Combining results from two or
more 2D images using a similar weighting scheme also outperforms a
single 2D image, and (4) Combined 2D+3D outperforms the multi-image
2D result. This is the first (so far, only) work to present such an
experimental control to substantiate multi-modal performance improvement."
-
Ensemble Diversity Measures and Their Application to Thinning,
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer,
and W. Philip Kegelmeyer,
Information Fusion
6 (1), March 2005, 49-62.
pdf of this paper.
... We evaluate thinning algorithms
on ensembles created by several techniques on 22
publicly available datasets.
When compared to other methods, our percentage correct
diversity measure
algorithm shows a greater correlation between the increase
in voted ensemble
accuracy and the diversity value. ...
Finally, the
methods proposed for thinning again show that ensembles can be made
smaller without loss in accuracy.
-
Improved Range Image Segmentation by Analyzing Surface Fit Patterns,
Jaesik Min and Kevin W. Bowyer,
Computer Vision and Image Understanding 97(2),
February 2005, 242-258.
pdf of this paper.
We propose a new approach to range image segmentation of planar and curved surface scenes.
Our method is mainly an extended design of an existing algorithm, which was guided by a
framework of performance evaluation. We choose the range segmentation algorithm
developed by Jiang and Bunke as our baseline algorithm, which is fast and has shown
relatively high performance in several experimental performance evaluation studies.
We analyze the types of errors made by the algorithm, propose design modifications to
decrease the error rate, and experimentally verify that the new approach achieves
statistically significant performance improvement. Whereas the baseline algorithm
applies the edge-linking uniformly to all edge pixels to segment a region, the
modified algorithm selects high potential edge areas in the region by analyzing the
surface fit pattern and gives priority of edge-linking to those areas. The
contributions of this work are (1) an improved algorithm for segmentation of range
images of both planar and curved surface scenes, and (2) a demonstration of using
empirical performance evaluation to guide algorithm design and modification to achieve
better performance.
-
The Human ID Gait Challenge Problem: Data Sets, Performance, and Analysis,
Sudeep Sarkar, P. Jonathon Phillips, Zongyi Liu, Isidro Robledo,
Patrick Grother, and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine
Intelligence 27 (2), February 2005, 162-177.
pdf of this paper.
... To provide a means for measuring progress and characterizing
the properties of gait recognition, we introduce the HumanID Gait
Challenge Problem. The challenge problem consists of a baseline
algorithm, a set of 12 experiments, and a large data set.
The baseline algorithm estimates silhouettes by background
subtraction and performs recognition by temporal correlation of
silhouettes. The 12 experiments are of increasing difficulty,
as measured by the baseline algorithm, and examine the effects
of five covariates on performance. ...
-
Comments on "A Parallel Mixture of SVMs for Very
Large Scale Problems,"
Xiaomei Liu, Lawrence O. Hall, and Kevin W. Bowyer,
Neural Computation
16 (7), July 2004, 1345-1351.
pdf of this paper.
... Experiments on the Forest Cover data set show that this
parallel mixture is more
accurate than a single SVM, with 90.72% accuracy reported
on an independent test set.
While this accuracy is impressive, the referenced paper
does not consider alternative
types of classifiers. In this comment, we show that a simple
ensemble of decision
trees results in a higher accuracy, 94.75%, and is
computationally efficient. This result
is somewhat surprising and illustrates the general value
of experimental comparisons using different types of classifiers.
-
Learning Ensembles from Bites: a Scalable and Accurate Approach,
Nitesh Chawla, Lawrence O. Hall, Kevin W. Bowyer
and W. Philip Kegelmeyer,
Journal of Machine Learning Research
5, April 2004, 421-451.
pdf of this paper.
... Voting many classifiers built on small subsets of data
is a promising approach for learning from massive data sets,
one that can utilize
the power of boosting and bagging. We propose a framework for
building hundreds or thousands
of such classifiers on small subsets of data in a distributed
environment. Experiments show this
approach is fast, accurate, and scalable.
-
Face Recognition Technology and the Security Versus Privacy Tradeoff,
Kevin W. Bowyer,
IEEE Technology and Society, Spring 2004, 9-20.
pdf of this paper.
Video surveillance and face recognition systems have
become the subject
of increased interest and controversy after the September 11 terrorist
attacks on the United States. ...
This paper analyzes the interplay of technical and social issues
involved in the widespread application of video surveillance for
person identification.
This paper received a 2005 Award of Excellence from the Society for Technical Communication.
-
Automated Performance Evaluation of Range Image Segmentation Algorithms,
Jaesik Min, Mark Powell, and Kevin W. Bowyer,
IEEE Transactions on Systems, Man, and Cybernetics - Part B,
34 (1), February 2004, 263-271.
pdf of this paper.
... We present an automated framework for evaluating the performance
of range image segmentation algorithms. Automated tuning of algorithm parameters in
this framework results in performance as good as that previously obtained with
careful manual tuning by the algorithm developers. ... This framework is demonstrated
using range images, but in principle it could be used to evaluate region segmentation
algorithms for any type of images.
-
Assessment of Time Dependency in Face Recognition: An Initial Study,
Patrick Flynn, Kevin W. Bowyer and P. Jonathon Phillips,
Audio- and Video-Based Biometric Person Authentication (AVBPA 2003),
Springer Lecture Notes in Computer Science 2688, 44-51.
pdf of this paper.
... Experimental results suggest that (a) recognition performance is
substantially poorer when unknown images are acquired on a different day
from the enrolled images, (b) degradation in performance does not follow
a simple predictable pattern with time between known and unknown image
acquisition, and (c) performance figures quoted in the literature based
on known and unknown image sets acquired on the same day may have little
practical value.
-
Is Error-based Pruning Redeemable?,
Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield and Steven Eschrich,
and Richard Collins,
International Journal of Artificial Intelligence Tools,
12 (3), September 2003, 249-264.
pdf of this paper.
-
Comparison and Combination of Ear and Face Images for Appearance-based Biometrics,
Kyong Chang, Kevin W. Bowyer, Sudeep Sarkar, and Barnabas Victor,
IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (9),
September 2003, 1160-1165.
pdf of this paper.
In the experiments reported here, recognition performance
is essentially identical using ear images or face images and combining the two for
multimodal recognition results in a statistically significant performance
improvement. ... To our knowledge, ours is the only work to present any experimental
results of computer algorithms for biometric recognition based on the ear.
-
Face Recognition Using 2D and 3D Facial Data,
Kyong I. Chang, Kevin W. Bowyer and Patrick J. Flynn,
First Workshop on Multi-Modal User Authentication , Santa Barbara, 25-32, December 2003.
pdf of this paper.
Results are presented for the largest experimental study to date that investigates
the comparison and combination of 2D and 3D face recognition. To our knowledge, this
is the only such study to incorporate significant time lapse between gallery and probe
image acquisition ...
Reprinted in the Journal of Intelligence Community Research and Development.
-
Distributed Learning with Bagging-like Performance,
Nitesh Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer,
and Clayton Springer,
Pattern Recognition Letters 24 (1-3), 2003, 455-471.
pdf of this paper.
Bagging forms a committee of classifiers by bootstrap aggregation of training sets
from a pool of training data. A simple alternative to bagging is to partition the
data into disjoint subsets. Experiments with decision tree and neural network
classifiers on various datasets show that, given the same size partitions and bags,
disjoint partitions result in performance equivalent to, or better than, bootstrap
aggregates (bags). Many applications (e.g., protein structure prediction) involve
use of datasets that are too large to handle in the memory of the typical computer.
Hence, bagging with samples the size of the data is impractical. Our results indicate
that, in such applications, the simple approach of creating a committee of n
classifiers from disjoint partitions each of size 1/n (which will be memory resident
during learning) in a distributed way results in a classifier which has a bagging-like
performance gain. The use of distributed disjoint partitions in learning is significantly
less complex and faster than bagging.
-
SMOTE: Synthetic Minority Over-sampling TEchnique,
Nitesh Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer,
Journal of Artificial Intelligence Research 16, 2002, 321-357.
pdf of this paper.
This paper shows that a combination of our method of over-sampling
the minority (abnormal) class and under-sampling the majority (normal) class can achieve
better classifier performance (in ROC space) than only under-sampling the majority class.
This paper also shows that a combination of our method of over-sampling the minority class
and under-sampling the majority class can achieve better classifier performance (in ROC
space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method
of over-sampling the minority class involves creating synthetic minority class examples.
-
The Gait Identification Challenge Problem:
Data Sets and Baseline Algorithm,
P. Jonathon Phillips, Sudeep Sarkar, Isidro Robledo,
Patrick Grother, and Kevin W. Bowyer,
International Conference on Pattern Recognition (ICPR 2002)
, August 2002, Montreal, I:385-388.
-
``Star Wars'' Revisited - A Continuing Case Study In Ethics and Safety-Critical Software,
Kevin W. Bowyer,
IEEE Technology and Society 21 (1), Spring 2002, 13-26.
pdf of this paper.
The Reagan-era Strategic Defense Initiative was the focus of
a great deal of technical argument relating to the design and testing of safety
critical software. ... This paper describes a curriculum module developed around
a Reagan-era SDI debate on the theme - 'Star Wars: Can the computing requirements
be met?' This module should be appropriate for use in ethics-related or
software-engineering-related courses taught in undergraduate Information Systems,
Information Technology, Computer Science, or Computer Engineering programs.
This article is highlighted on the
SSIT web site
as one related to ABET / CSAB accreditation requirements.
-
Edge Detector Evaluation Using Empirical ROC Curves,
Kevin W. Bowyer, Christine Kranenburg, and Sean Dougherty.
Computer Vision and Image Understanding 84 (1), October 2001, 77-103.
pdf of this paper.
This paper focuses on evaluating the performance of
edge detection algorithms. ... Performance is summarized using receiver
operating characteristic curves. ... No other work uses adaptive parameter
sampling to construct empirical ROC curves for pixel-level performance evaluation,
or has used so many real images, or has compared such a broad selection of detectors.
-
Comparison of Edge Detector Performance Through Use In An Object Recognition Task,
Min C. Shin, Dmitry B. Goldgof and Kevin W. Bowyer.
Computer Vision and Image Understanding 84 (1), October 2001, 160-178.
DOI link.
Edge detector performance is measured using a particular edge-based object recognition
algorithm as a higher-level task. A detector's performance is ranked according to the
object recognition performance that it generates. We have used a challenging train
and test dataset containing 110 images of jeep-like objects. Six edge detectors are
compared and results suggest that (1) the SUSAN edge detector performs best and (2)
the ranking of various edge detectors is different from that found in other
evaluations.
-
Comparison of Edge Detection Algorithms Using a Structure From Motion Task,
Min C. Shin, Dmitry B. Goldgof, Kevin W. Bowyer and S. Nikiforou,
IEEE Transactions on Systems, Man and Cybernetics - Part B 31 (4), August 2001, 589-601.
DOI link.
We use the task of structure from motion as a "black box" through which to evaluate
the performance of edge detection algorithms. Edge detector goodness is measured by how
accurately the SFM could recover the known structure and motion from the edge detection
of the image sequences. We use a variety of real image sequences with ground truth to evaluate
eight different edge detectors from the literature. Our results suggest that ratings of edge
detector performance based on pixel-level metrics and on the SFM are well correlated and that
detectors such as the Canny detector and the Heitger detector offer the best performance.
-
Mentoring undergraduates in computer vision research,
Mubarak Shah and Kevin W. Bowyer,
IEEE Transactions on Education 44 (3), 252-257, August 2001.
DOI link.
... During the last 14 years roughly 130 undergraduate students from several
institutions have participated in the research experiences for undergraduates
(REU) program funded by the National Science Foundation (NSF). A large fraction
of our students have been able to prepare a paper for submission to a conference,
have the paper accepted, and then attend the conference to present the paper. ...
-
Ethics and Computing: Living Responsibly In a Computerized World,
Kevin W. Bowyer.
IEEE Press (second edition), 2001.
-
The Digital Database for Screening Mammography,
Michael Heath, Kevin Bowyer, Daniel Kopans, Richard Moore and W. Philip Kegelmeyer.
in Proceedings of the Fifth International Workshop on Digital Mammography,
M.J. Yaffe, ed., 212-218, Medical Physics Publishing, 2001.
pdf of this paper.
The Digital Database for Screening Mammography (DDSM) is a database of digitized
film-screen mammograms with associated ground truth information. The purpose of this
resource is to provide a large set of mammograms in a digital format that may be used by
researchers to evaluate and compare the performance of computer-aided detection (CAD)
algorithms. ...
-
Validation of Medical Image Analysis Techniques,
Kevin W. Bowyer,
chapter in Handbook of Medical Imaging: Volume 2 - Medical Image Processing and Analysis
J.M. Fitzpatrick and M. Sonka, editors, SPIE Press, 2000, 567-607.
link to copy on Google Books.
-
Resources For Teaching Ethics and Computing,
Kevin W. Bowyer,
Journal of Information Systems Education 11 (3-4), 91-92, Summer-Fall 2000.
-
Pornography On the Dean's PC: An Ethics and Computing Case Study,
Kevin W. Bowyer,
Journal of Information Systems Education 11 (3-4), 121-126, Summer-Fall 2000.
JISE link.
-
Themes for Improved Teaching of Image Computation,
Kevin W. Bowyer, George Stockman and Louise Stark,
IEEE Transactions on Education 43 (2), 221-223, May 2000.
DOI link.
This paper reports on recommendations and results from a panel discussion and a workshop
devoted to the theme of education in areas that involve image computation. One specific
set of contributions is in the area of integrating image computation into required core
courses such as Introduction to Computing and Data Structures. Another area of specific
contributions is the development of undergraduate elective courses on topics such as
robotics and medical image analysis. A third area of contributions is the improvement of
traditional image processing and computer vision courses.
-
Registration and Difference Analysis of Corresponding Mammogram Images,
Maha Y. Sallam and Kevin W. Bowyer,
Medical Image Analysis 3 (2), 103-118, 1999.
DOI link.
An automated technique is proposed for identifying differences between
corresponding mammogram images. The technique recovers an approximate
deformation between a pair of mammograms based on identifying
corresponding features across the two images.
The registration process is completed using an unwarping technique for
transforming one image into the coordinate system of the other.
A difference image is generated using intensity-weighted subtraction
in order to identify regions of large difference.
Evaluation of the technique is performed using 124 bilateral image pairs
which contain a total of 77 abnormalities of different types.
The purpose of this paper is to measure the extent to which the mammogram
registration technique is able to provide useful information for
identifying abnormalities in mammograms.
-
Evaluation of Texture Segmentation Algorithms,
Kyong Chang, Kevin W. Bowyer and S. Munishkumaran,
Computer Vision and Pattern Recognition (CVPR '99),
I:294-299.
pdf of this paper.
This paper presents a method of evaluating unsupervised texture segmentation algorithms.
The control scheme of texture segmentation has been conceptualized as two modular
processes: (1) feature computation and (2) segmentation of homogeneous regions based
on the feature values. Three feature extraction methods are considered: gray level
co-occurrence matrix, Laws' texture energy and Gabor multi-channel filtering.
Three segmentation algorithms are considered: fuzzy c-means clustering, square-error
clustering and split-and-merge. A set of 35 real scene images with manually-specified
ground truth was compiled. Performance is measured against ground truth on real images
using region-based and pixel-based performance metrics.
-
Dynamic-Scale Model Construction From Range Imagery,
Adam Hoover Dmitry Goldgof, and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
29 (12), 1352-1357, December 1998.
DOI link.
The construction of a surface model from range data may be undertaken at any
point in a continuum of scales that reflects the level of detail of the
resulting model. This continuum relates the construction parameters to the
scale of the model. We propose methods to dynamically reprocess range data
at different scales. The construction result from a single scale is automatically
evaluated, causing reconstruction at a different scale when user-defined criteria
are not met. We demonstrate our methods in constructing a planar b-rep space
envelope (a scene representation) for over 400 range images. The experiments
demonstrate that ability to construct 100 percent valid models, with the scale
of detail within specified requirements.
-
Overview of Work In Empirical Evaluation of Computer Vision Algorithms,
Kevin W. Bowyer and P. Jonathon Phillips,
in Empirical Evaluation Techniques in Computer Vision,
K.W. Bowyer and P.J. Phillips, editors,
IEEE Computer Society Press, 1998, pages 1-11.
pdf version of this chapter.
-
Function from Visual Analysis and Physical Interaction: A Methodology for
Recognition of Generic Classes of Objects,
Melanie Sutton, Louise Stark and Kevin Bowyer,
Image and Vision Computing 16 (11), 745-763, August 1998.
DOI link.
This paper presents an overview of the GRUFF-I (Generic Recognition Using
Form, Function and Interaction) system, a nonpart-based approach to generic
object recognition which reasons about and generates plans for interaction
with three-dimensional (3D) shapes from the categories furniture and dishes.
...
-
Current Status of the Digital Database for Screening Mammography,
Michael Heath, Kevin Bowyer, Daniel Kopans, W. Philip Kegelmeyer, Richard Moore,
Kyong Chang and S. Munishkumaran,
in Digital Mammography, 457-460, Kluwer Academic Publishers, 1998
(proceedings of the Fourth International Workshop on Digital Mammography)
pdf of this paper.
The Digital Database for Screening Mammography is a resource for use by researchers investigating mammogram
image analysis. In particular, the resource is focused on the context of image analysis to aid is screening
for breast cancer. The database now contains substantial numbers of "normal" and "cancer" cases. ...
-
Are Edges Sufficient for Object Recognition?,
Thomas A. Sanocki, Kevin W. Bowyer, Michael Heath, and Sudeep Sarkar,
Journal of Experimental Psychology:
Human Perception and Performance
24 (1), 340-349, January 1998.
pdf of this paper.
The authors argue that the concept of `edges' as used in
current research on object recognition obscures the significant difficulties
involved in interpreting stimulus information. ... With 1-s exposures, the accuracy
of identifying objects in the edge images was found to be less than half that with
color photographs. Therefore, edges are far from being sufficient for object
recognition.
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Comparison of Edge Detectors: A Methodology and Initial Study,
Michael Heath, Sudeep Sarkar, Thomas A. Sanocki and Kevin W. Bowyer,
Computer Vision and Image Understanding
69, (1), 38-54, January 1998.
DOI link.
Because of the difficulty of obtaining ground truth for real images, the traditional
technique for comparing low-level vision algorithms is to present image results,
side by side, and to let the reader subjectively judge the quality. This is not a
scientifically satisfactory strategy. However, human rating experiments can be done
in a more rigorous manner to provide useful quantitative conclusions. We present a
paradigm based on experimental psychology and statistics, in which humans rate the
output of low level vision algorithms. We demonstrate the proposed experimental
strategy by comparing four well-known edge detectors: ...
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A Robust Visual Method for Assessing the Relative
Performance of Edge-Detection Algorithms,
Michael Heath, Sudeep Sarkar, Thomas A. Sanocki, and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
19 (12), 1338-1359, December 1997.
pdf of this paper.
A new method for evaluating edge detection is presented
... The basic measure of performance is a visual rating score which indicates
the perceived quality of the edges for identifying an object. ... The novel
aspect of this work is the use of a visual task and real images of complex
scenes in evaluating edge detectors.
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Generating ROC curves for artificial neural networks,
Kevin S. Woods and Kevin W. Bowyer,
IEEE Transactions on Medical Imaging 16 (3), 329-337, June 1997.
DOI link.
Receiver operating characteristic (ROC) analysis is an established method
of measuring diagnostic performance in medical imaging studies.
Traditionally, artificial neural networks (ANN's) have been applied as a
classifier to find one "best" detection rate. Recently researchers have
begun to report ROC curve results for ANN classifiers. The current standard
method of generating ROC curves for an ANN is to vary the output node
threshold for classification. Here, the authors propose a different
technique for generating ROC curves for a two class ANN classifier.
They show that this new technique generates better ROC curves in the
sense of having greater area under the ROC curve (AUC), and in the
sense of being composed of a better distribution of operating points.
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Combination of Multiple Classifiers Using Local Accuracy Estimates,
Kevin S. Woods, W. Philip Kegelmeyer and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
19 (4), 405-410, April 1997.
pdf of this paper.
This paper presents a method for combining classifiers that uses estimates of each
individual classifier's local accuracy in small regions of the feature space
surrounding an unknown sample. An empirical evaluation using five real data sets
confirms the validity of our approach compared to some other combination of multiple
classifiers algorithms. We also suggest a methodology for determining the best mix
of individual classifiers.
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Recognizing object function through reasoning about partial shape
descriptions and dynamic physical properties,
Louise Stark, Kevin W. Bowyer, Adam W. Hoover and Dmitry B. Goldgof,
Proceedings of the IEEE
84 (11), 1640-1656, November 1996.
DOI link.
Knowledge about required functionality of an object can be used as an effective
representation for a generic object category (e.g., "chair", "cup", or "hammer").
This approach to object representation and recognition has recently become an
active area of research. We explore a scenario in which a robot senses the
environment to obtain an initial partial shape model of an object. If the
information in this initial model is not sufficient to hypothesize a possible
function for the object, then additional view(s) may be suggested. Once a
possible function is hypothesized, a plan is formulated for interacting with
the object to confirm that its material properties are compatible with the
hypothesized function. The module for reasoning about partial shape models
has been evaluated on over 200 shape models acquired from range images.
The module for carrying out a function verification plan has been evaluated
in a simulated environment using the ThingWorld (TW) system.
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An Experimental Comparison of Range Image Segmentation Algorithms,
Adam W. Hoover, Gillian Jean-Baptiste, Xiaoyi Jiang, Patrick Flynn, Horst Bunke,
Dmitry Goldgof, Kevin W. Bowyer, David Eggert, Andrew Fitzgibbon, and Robert Fisher,
IEEE Transactions on Pattern Analysis and Machine Intelligence
18, (7), 673-689, July 1996.
pdf of this paper.
This paper evaluates four segmentation algorithms on
80 real images with ground truth and objective performance measures. ... This type
of framework for a competitive effort is essentially never used in mainstream
computer vision, though it is standard practice in some related areas ... Beside
the development of a philosophy of comparative experiment research, an important
contribution here is an assessment of the state-of-the-art in planar range image
segmentation. Based on our results, we assert that this problem is not 'solved.'
This finding may be surprising and possibly controversial. We would welcome an
empirical demonstration that the claim is false.
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Learning Membership Functions in a Function-based Object Recognition System,
Kevin S. Woods, Diane Cook, Lawrence Hall, Louise Stark and Kevin W. Bowyer,
Journal of Artificial Intelligence Research 3, 187-222, October 1995.
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On recovering hyperquadrics from range data,
Senthil Kumar, Song Han, Dmitry Goldgof, and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
17, (11), 1079-1083, November 1995.
Computer Vision and Image Understanding 62 (2), 177-193, September 1995.
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Extracting a Valid Boundary Representation From a Segmented Range Image,
Adam W. Hoover, Dmitry Goldgof, and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
17, (9), 920-924, September 1995.
A new approach is presented for extracting an explicit 3-D
shape model from a single range image. One novel aspect is that the model
represents both observed object surfaces, and surfaces which bound the volume
of occluded space. Another novel aspect is that the approach does not require
that the range image segmentation be perfect. ... A third novel aspect of this
work is that the implementation has been evaluated on over 200 real images of
polyhedral objects with no operator intervention and all parameters held constant,
and obtained a 97% success rate in creating valid b-reps.
DOI link.
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Generic Recognition of Articulated Objects Through Reasoning About Potential Function,
Kevin Green, David Eggert, Louise Stark, and Kevin W. Bowyer,
Computer Vision and Image Understanding 62 (2), 177-193, September 1995.
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Aspect Graphs and Their Use in Object Recognition,
David Eggert, Louise Stark, and Kevin W. Bowyer,
Annals of Mathematics and Artificial Intelligence 13, 347-375, 1995.
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GRUFF-3: Generalizing the Domain of a Function-based Recognition System,
Melanie Sutton, Louise Stark, and Kevin W. Bowyer,
Pattern Recognition 27 (12), 1743-1766, December 1994.
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Function-based Generic Recognition for Multiple Object Categories,
Louise Stark and Kevin W. Bowyer,
CVGIP: Image Understanding 59 (1), 1-21, January 1994.
... Our work concentrates specifically on the relation between
shape and function of rigid 3D objects. Recognition of an observed
shape is performed by reasoning about the function that it might
serve. Previous efforts have dealt with only a single basic level
object category. A number of important issues arise in extending
this approach to deal with multiple basic-level categories. ...
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Comparative evaluation of pattern recognition techniques for
detection of microcalcifications in mammography,
Kevin S. Woods, Christopher C. Doss, Kevin W. Bowyer, Jeffrey L. Solka,
Carey E. Priebe, and W. Philip Kegelmeyer,
International Journal of Pattern Recognition and Artificial Intelligence
7 (6), 1417-1436, December 1993.
Computer-assisted detection of microcalcifications in mammographic images
will likely require a multistage algorithm that includes segmentation of
possible microcalcifications, pattern recognition techniques to classify
the segmented objects, a method to determine if a cluster of calcifications
exists, and possibly a method to determine the probability of malignancy.
This paper focuses on the first three of these stages ...
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The Scale Space Aspect Graph,
David W. Eggert, Kevin W. Bowyer, Charles R. Dyer, Henrik I. Christensen, and Dmitry B. Goldgof,
IEEE Transactions on Pattern Analysis and Machine Intelligence
15 (11), 1114-1130, November 1993.
pdf of this paper.
This paper introduces the concept of the scale space aspect
graph, defines three different interpretations of the scale dimension, and presents a
detailed example for a simple class of objects, with scale defined in terms of the spatial
extent of features in the image.
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An Investigation of Methods of Combining Functional Evidence for 3-D Object Recognition,
Louise Stark, Lawrence O. Hall, and Kevin W. Bowyer,
International Journal of Pattern Recognition and Artificial Intelligence
7 (3), 573-594, June 1993.
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Computing the Generalized Aspect Graph for Objects with Moving Parts,
Kevin W. Bowyer, Maha Y. Sallam, David Eggert, and John H. Stewman,
IEEE Transactions on Pattern Analysis and Machine Intelligence
15 (6), 605-610, June 1993.
A number of researchers have described algorithms for
computing the aspect graph representation, but this work has thus far
been limited to entirely rigid objects. We generalize this concept to
include a larger, more realistic domain of objects known as articulated
assemblies, those objects composed of rigid parts with articulated
connections allowed between parts.
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Computing the Perspective Projection Aspect Graph of Solids of Revolution,
David W. Eggert and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
15 (2), 109-128, February 1993.
This paper presents the first (only) implemented
algorithm to compute the aspect graph for a class of curved-surface
objects based on an exact parcellation of 3-D viewpoint space. The
class of objects considered is solids of revolution. ... the worst-case
complexity of ... the number of cells in the aspect graph is is O(N^4),
where N is the degree of a polynomial that defines the object shape.
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Achieving Generalized Object Recognition Through Reasoning
About Association of Function To Structure,
Louise Stark and Kevin W. Bowyer,
IEEE Transactions on Pattern Analysis and Machine Intelligence
13 (10), 1097-1104, October 1991.
pdf of this paper.
The purpose of the work described here is to demonstrate the
feasibility of defining an object category in terms of the functional properties
shared by all objects in the category. ... A complete system has been implemented
that takes the boundary surface description of a 3-D object as its input and
attempts to recognize whether the object belongs to the category chair ... This is,
to our knowledge the first (only) implemented system to explore the use of a purely
function-based definition of an object category ... to recognize 3-D objects.
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Aspect Graphs: An Introduction and Survey of Recent Results,
Kevin W. Bowyer and Charles R. Dyer,
International Journal of Imaging Systems and Technologies 2, 315-328, 1990.
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Direct Construction of Perspective Projection Aspect Graphs for Planar-Face
Convex Objects,
John Stewman and Kevin W. Bowyer,
Computer Vision, Graphics and Image Processing 51 (1), 20-37, July 1990.
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The Effects of Age on Radionuclide Angiographic Detection and
Quantitation of Left-to-right Shunts,
Page A.W. Anderson, Kevin W. Bowyer, and Robert H. Jones,
American Journal of Cardiology 53, 879-883, March 1984.
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Optimizing Contiguous Element Region Selection for Virtual Memory Computer Systems,
Kevin W. Bowyer, and C. Frank Starmer,`
IEEE Transactions on Computers 32, 1201-1203, December 1983.
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Radionuclide Analysis of Right and Left Ventricular Response to
Exercise in Patients with Atrial and Ventricular Septal Defects,
Claude A. Peter, Kevin W. Bowyer, and Robert H. Jones,
American Heart Journal 105, 428-435, March 1983.
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Error Sensitivity of Computed Tomography Guided Stereotaxis,
Kevin W. Bowyer, C. Frank Starmer and P. J. DuBois,
Computers and Biomedical Research 15, 272-280, 1982.
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A Simulation-based Sensitivity Study of Angiocardiographic Approaches to Shunt Assessment,
Kevin W. Bowyer and C. Frank Starmer,
Computers and Biomedical Research 15, 111-128, 1982.
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CT-guided Stereotaxis Using a Modified Conventional Stereotaxic Frame,
P. J. DuBois, B. S. Nashold, J. Perry, P. Burger, Kevin W. Bowyer,
E. R. Heinz, B. P. Drayer, S. Bigner, and A. C. Higgins,
American Journal of Neuroradiology 3, 345-351, 1982.
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