Karen Hollingsworth

Karen Hollingsworth
University of Notre Dame | ND · Department of Computer Science and Engineering

About

27
Publications
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1,976
Citations

Publications

Publications (27)
Article
The periocular region is the part of the face immediately surrounding the eye, and researchers have recently begun to investigate how to use the periocular region for recognition. Understanding how humans recognize faces helped computer vision researchers develop algorithms for face recognition. Likewise, understanding how humans analyze periocular...
Article
A recent survey of iris biometric research from its inception through 2007, roughly 15 years of research, lists approximately 180 publications. This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008–2010. Research in iris biometrics has expanded so much that although covering only...
Article
High-quality periocular images have sufficient variability between people that they can be used for automated recognition. However, there are no standards for training people to compare periocular images in a forensic context. Potential methods for training people to compare eyebrow images are investigated in this article. Pairs of images were pres...
Conference Paper
Prior research has shown that manually-segmented eyebrows can be used for recognition purposes. However, eyebrow recognition is not as useful without an automated segmentation algorithm. We propose a method to automatically outline the eyebrows in a face using active shape models. We train several models using the images from the Face Recognition G...
Article
A recent survey of iris biometric research from its inception through 2007, roughly 15 years of research, lists approximately 180 publications. This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008 to 2010. Research in iris biometrics has expanded so much that, although covering...
Article
Full-text available
Periocular biometrics is the recognition of individuals based on the appearance of the region around the eye. Periocular recognition may be useful in applications where it is difficult to obtain a clear picture of an iris for iris biometrics, or a complete picture of a face for face biometrics. Previous periocular research has used either visible-l...
Article
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 exa...
Article
The periocular region is the part of the face immediately surrounding the eye, and researchers have recently begun to investigate how to use the periocular region for recognition. Understanding how humans recognize faces helped computer vision researchers develop algorithms for face recognition. Likewise, understanding how humans analyze periocular...
Article
The most common iris biometric algorithm represents the texture of an iris using a binary iris code. Not all bits in an iris code are equally consistent. A bit is deemed fragile if its value changes across iris codes created from different images of the same iris. Previous research has shown that iris recognition performance can be improved by mask...
Conference Paper
The periocular region is the part of the face immediately surrounding the eye, and researchers have recently begun to investigate how to use the periocular region for recognition. Understanding how humans recognize faces helped computer vision researchers develop algorithms for face recognition. Likewise, understanding how humans analyze periocular...
Conference Paper
Prior research has shown that the textural detail of the iris is sufficiently distinctive to distinguish identical twin siblings. However, no research has addressed the question of whether twins' irises are sufficiently similar in some sense to correctly determine that two irises are from twins. We conducted a human classification study in which pa...
Article
Today, campus grids provide users with easy access to thousands of CPUs. However, it is not always easy for nonexpert users to harness these systems effectively. A large workload composed in what seems to be the obvious way by a naive user may accidentally abuse shared resources and achieve very poor performance. To address this problem, we argue t...
Article
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. For comparison, we reimplement three score-level fusion methods (Ma, Krichen, and Schmid). We find that our signal-level fusion of N images performs...
Article
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 i...
Conference Paper
The most common iris biometric algorithm represents the texture of an iris using a binary iris code. Not all bits in an iris code are of equal value. A bit is deemed fragile if it varies in value across iris codes created from different images of the same iris. Previous research has shown that iris recognition performance can be improved by masking...
Article
Iris biometric systems apply filters to iris images to extract information about iris texture. Daugman's approach maps the filter output to a binary iris code. The fractional Hamming distance between two iris codes is computed and decisions about the identity of a person are based on the computed distance. The fractional Hamming distance weights al...
Conference 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 than methods based on single still images, and better than previously published multi-gallery score- fusion m...
Article
Many security applications require accurate identification of people, and research has shown that iris biometrics can be a powerful identification tool. However, in order for iris biometrics to be used on larger populations, error rates in the iris biometrics algorithms must be as low as possible. Furthermore, these algorithms need to be tested in...
Article
Iris biometrics research has largely ignored the problems associated with variations in pupil dilation between the enrollment image and the image to be recognized or verified. Indeed, in most current sys- tems, information about pupil dilation is discarded when the iris region is normalized to a dimensionless polar coordinate system from which the...
Conference Paper
Previous research has assumed that all parts of an iris code are equally valuable. Alternatively, some researchers claim that parts of the iris are more valuable, but they still use the same portions of the iris for all subjects. Our research, presented originally, is the first and only work to show experimentally that some bits in the iris code ar...
Article
This survey covers the historical development and current state of the art in image understanding for iris biometrics. 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....
Article
This work studies the effect of pupil dilation on the accuracy of iris biometrics. We find 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. Another experimental result is that even when the degree of dila...
Conference Paper
Many iris recognition systems use filters to extract information about the texture of an iris image. In the Daugman-style approach, the filter output is mapped to a binary iris code. The normalized Hamming distance between two iris codes is computed and decisions about the identity of a person are based on the computed distance. The normalized Hamm...
Article
Abstract by Karen P. Hollingsworth Two decades ago, the United States issued the first patent claiming the idea of an automated iris biometrics system [1]. Today, multiple companies offer com- mercial biometrics products. However, there are still many unanswered questions about the workings of the iris biometrics algorithms. Before iris recognition...

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