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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 76 - 100 of 129

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

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

The Iris Challenge Evaluation 2005

September 29, 2008
Author(s)
P J. Phillips, Kevin W. Bowyer, Patrick J. Flynn, Xiaomei Liu, W T. Scruggs
This paper describes the Iris Challenge Evaluation (ICE) 2005. The ICE 2005 contains a dataset of 2953 iris images from 132 subjects. The data is organized into two experiments: right and left eye. Iris recognition performance is presented for twelve

Focus on Quality, Predicting FRVT 2006 Performance

September 17, 2008
Author(s)
P J. Phillips, J. R. Beveridge, Geof H. Givens, Bruce A. Draper, Yui M. Lui
This paper summarizes a study carried out on data from the Face Recognition Vendor Test 2006 (FRVT 2006). The finding of greatest practical importance is the discovery of a strong connection between a relatively simple measure of image quality and

Humans versus Algorithms: Comparisons from the Face Recognition Vendor 2006

September 17, 2008
Author(s)
P J. Phillips, Alice J. O'Toole, Abhijit Narvekar
We present a synopsis of results comparing the performance of humans with face recognition algorithms tested in the Face Recognition Vendor Test (FRVT) 2006 and Face Recognition Grand Challenge (FRGC). Algorithms and humans matched face identity in images

Subspace Linear Discriminant Analysis for Face Recognition

June 10, 2008
Author(s)
W Zhao, R Chellappa, P. Jonathon Phillips
In this correspondence, we describe a holistic face recognition method based on subspace Linear Discriminant Analysis (LDA). Like existing methods, this method consists of two steps: first, the face image is projected into a face subspace via Principal