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Information Technology

Biometrics

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Displaying 401 - 425 of 804

Robust Iris Recognition Baseline for the Grand Challenge

May 17, 2011
Author(s)
Yooyoung Lee, Ross J. Micheals, James J. Filliben, P J. Phillips
Due to its distinctiveness, the human iris is a popular biometric feature used to identity a person with high accuracy. The “Grand Challenge” in iris recognition is to have an effective algorithm for subject verification or identification under a broad

Distinguishing Identical Twins by Face Recognition

March 21, 2011
Author(s)
P J. Phillips, Patrick J. Flynn, Kevin W. Bowyer, Richard W. Vorder Bruegge, Patrick J. Grother, George W. Quinn, Matthew Pruitt
The paper measures the 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

An Introduction to the Good, the Bad, & the Ugly Face Recognition Challenge Problem

March 16, 2011
Author(s)
P J. Phillips, J. R. Beveridge, Bruce A. Draper, Geof H. Givens, Alice J. O'Toole, David Bolme, Joseph Dunlop, Yui M. Lui, Hassan A. Sahibzada, Samuel Weimer
The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, & the Ugly consists of three partitions. The

When High-Quality Face Images Match Poorly

March 13, 2011
Author(s)
J. R. Beveridge, P. Jonathon Phillips, Geof H. Givens, Bruce A. Draper, Mohammad N. Teli, David Bolme
In face recognition, quality is typically thought of as a property of individual images, not image pairs. The implicit assumption is that high-quality images should be easy to match to each other, while low quality images should be hard to match. This

Demographic Effects on Estimates of Automatic Face Recognition Performance

March 11, 2011
Author(s)
Alice J. O'Toole, P. Jonathon Phillips, Xiaobo An, Joseph Dunlop
The intended applications of automatic face recognition systems include venues that vary widely in demographic diversity. Formal evaluations of algorithms do not commonly consider the effects of population diversity on performance. We document the effects

An Introduction to the Good, the Bad, & the Ugly Face Recognition Challenge Problem

March 10, 2011
Author(s)
P J. Phillips, J. R. Beveridge, Bruce A. Draper, Geof H. Givens, Alice J. O'Toole, David Bolme, Joseph Dunlop, Yui M. Lui, Hassan A. Sahibzada, Samuel Weimer
The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes in illumination that occur in still frontal faces. The Good, the Bad, & the Ugly consists of three

Demographic Effects on Estimates of Automatic Face Recognition Performance

March 10, 2011
Author(s)
Alice J. O'Toole, P. Jonathon Phillips, Xiaobo An, Joseph Dunlop
The intended applications of automatic face recognition systems include venues that vary widely in demographic diversity. Formal evaluations of algorithms do not commonly consider the effects of population diversity on performance. We document the effects

Empirical Evidence for Increased False Reject Rate with Time Lapse in ICE 2006

March 10, 2011
Author(s)
Sarah E. Baker, Patrick J. Flynn, Kevin W. Bowyer, P. Jonathon Phillips
We present results of the first systematic study to investigate the degree to which template aging occurs for iris biometrics. Our experiments use an image data set with approximately four years of elapsed time between the earliest and most recent images

Improving Face Recognition Technology

March 9, 2011
Author(s)
P J. Phillips
US-government sponsored evaluations and challenge problems have helped spur over two-orders-of-magnitude improvement in face recognition system performance.

When High-Quality Face Images Match Poorly

March 9, 2011
Author(s)
J. R. Beveridge, P. Jonathon Phillips, Geof H. Givens, Bruce A. Draper, Mohammad N. Teli, David Bolme
In face recognition, quality is typically thought of as a property of individual images, not image pairs. The implicit assumption is that high-quality images should be easy to match to each other, while low quality images should be hard to match. This