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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 151 - 175 of 242

Report on the Evaluation of 2D Still-Image Face Recognition Algorithms

June 17, 2010
Author(s)
Patrick J. Grother, George W. Quinn, P J. Phillips
The paper evaluates state-of-the-art face identification and verification algorithms, by applying them to corpora of face images the population of which extends into the millions. Performance is stated in terms of core accuracy and speed metrics, and the

Quantifying How Lighting and Focus Affect Face Recognition Performance

June 13, 2010
Author(s)
J. R. Beveridge, David Bolme, Bruce A. Draper, Geof H. Givens, Yui M. Lui, P. Jonathon Phillips
Recent studies show that face recognition in uncontrolled images remains a challenging problem, although the reasons why are less clear. Changes in illumination are one possible explanation, although algorithms developed since the advent of the PIE and

An Other-Race Effect for Face Recognition Algorithms

May 13, 2010
Author(s)
P J. Phillips, Alice J. O'Toole, Abhijit Narvekar, Fang Jiang, Julianne Ayadd
Psychology research has shown that human face recognition is more accurate for faces of one�s own race than for faces of other races. In recent years, interest in accurate computer-based face recognition systems has spurred the development of these systems

FRVT 2006: Quo Vidas Face Quality

May 10, 2010
Author(s)
P J. Phillips, J. R. Beveridge, Geof H. Givens, Bruce A. Draper, David Bolme, Yui M. Lui
This paper summarizes a study of how three state-of-the-art algorithms from the Face Recognition Vendor Test 2006 (FRVT 2006) are effected by factors related to face images and the people being recognized. The recognition scenario compares highly

Quantifying How Lighting and Focus Affect Face Recognition Performance

February 16, 2010
Author(s)
P J. Phillips, J. R. Beveridge, Bruce A. Draper, David Bolme, Geof H. Givens, Yui M. Lui
Recent studies show that face recognition in uncontrolled images remains a challenging problem, although the reasons why are less clear. Changes in illumination are one possible explanation, although algorithms developed since the advent of the PIE and

Improvements in Video-Based Automated System for Iris Recognition (VASIR)

December 7, 2009
Author(s)
Yooyoung Lee, Ross J. Micheals, P J. Phillips
Video-based Automated System for Iris Recognition (VASIR) performs two-eye detection, best quality image selection by adapting human vision and edge density methods, and iris verification. A new method of iris segmentation is implemented and evaluated that

An Other-Race Effect for Face Recognition Algorithms

August 19, 2009
Author(s)
P J. Phillips, Alice J. O'Toole, Fang Jiang, Abhijit Narvekar, Julianne Ayadd
Psychological research indicates that humans recognize faces of their own race more accurately than faces of other races. This "other-race effect" occurs for algorithms tested in a recent international competition for state-of-the-art face recognition

Factors that Influence Algorithm Performance

August 11, 2009
Author(s)
P J. Phillips, J. R. Beveridge, Geof H. Givens, Bruce A. Draper
A statistical study is presented quantifying the effects of covariates such as gender, age, expression, image resolution and focus on three face recognition algorithms. Specifically, a Generalized Linear Mixed Effect model is used to relate probability of

Overview of the Multiple Biometrics Grand Challenge

July 23, 2009
Author(s)
P J. Phillips, Patrick J. Flynn, J. R. Beveridge, Kevin W. Bowyer, W T. Scruggs, Alice O'Toole, David Bolme, Bruce A. Draper, Geof H. Givens, Yui M. Lui, Hassan A. Sahibzada, Joseph A. Scallan, Samuel Weimer
The goal of the Multiple Biometrics Grand Challenge (MBGC) is to improve the performance of face and iris recognition technology from biometric samples acquired under unconstrained conditions. The MBGC is organized into three challenge problems. Each

An Automated Video-Based System For Iris Recognition

June 2, 2009
Author(s)
Yooyoung Lee, P. Jonathon Phillips, Ross J. Micheals
We have successfully implemented a Video-based Automated System for Iris Recognition (VASIR), evaluating its successful performance on the MBGC dataset. The proposed method facilitates the ultimate goal of automatically detecting an eye area, extracting

Face Recognition by Computers and Humans

February 10, 2009
Author(s)
P J. Phillips, Rama Chellappa, Pawan Sinha
In most situations, identifying humans using faces is an effortless task for humans. Is this true for computers? This very question defines the field of automatic face recognition, one of the most active research areas in computer vision, pattern