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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 126 - 150 of 242

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

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

January 20, 2011
Author(s)
P J. Phillips, Kevin W. Bowyer, Patrick J. Flynn, Sarah E. Baker
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

Robust Iris Recognition Baseline for the Occular Challenge

January 20, 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 range

A Meta-Analysis of Face Recognition Covariates

November 22, 2010
Author(s)
Yui M. Lui, David Bolme, Bruce A. Draper, J. R. Beveridge, Geof H. Givens, P. Jonathon Phillips
This paper presents a meta-analysis for covariates that affect performance of face recognition algorithms. Our review of the literature found six covariates for which multiple studies reported effects on face recognition performance. These are: age of the

Performance Assessment of Face Recognition Using Super-Resolution

October 25, 2010
Author(s)
Shuowen Hu, Robert Maschal, S. S. Young, Tsai H. Hong, P. Jonathon Phillips
Recognition rate of face recognition algorithms is dependent on the resolution of the imagery, specifically the number of pixels contained within the face. Using a sequence of frames from low-resolution videos, super-resolution reconstruction can form a

Recognizing people from dynamic and static faces and bodies: Dissecting identity with a fusion approach

September 13, 2010
Author(s)
Alice J. O'Toole, P. Jonathon Phillips, Samuel Weimer, Dana A. Roark, Julianne Ayadd, Robert Barwick, Joseph Dunlop
The goal of this study was to evaluate human accuracy at identifying people from static and dynamic presentations of faces and bodies. Participants matched identity in pairs of videos depicting people in motion (walking or conversing) and in \best" static

Recognizing people from dynamic and static faces and bodies: Dissecting identity with a fusion approach

August 12, 2010
Author(s)
Alice J. O'Toole, P. Jonathon Phillips, Samuel Weimer, Dana A. Roark, Julianne Ayadd, Robert Barwick, Joseph Dunlop
The goal of this study was to evaluate human accuracy at identifying people from static and dynamic presentations of faces and bodies. Participants matched identity in pairs of videos depicting people in motion (walking or conversing) and in \best" static