U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: P. Jonathon Phillips (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 51 - 75 of 129

In-plane Rotation and Scale Invariant Clustering and Dictionary Learning

June 3, 2013
Author(s)
P J. Phillips, Challa Sastry, Yi-Chen Chen, Vishal M. Patel, Rama Chellappa
n this paper, we present an approach that simulta- neously clusters images and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. The main feature of the proposed

Video-based Face Recognition via Joint Sparse Representation

April 26, 2013
Author(s)
P J. Phillips, Yi-Chen Chen, Vishal M. Patel, Sumit Shekar, Rama Chellappa
In video-based face recognition, a key challenge is in exploiting the extra information available in a video; e.g., face, body, and motion identity cues. In addition, different video sequences of the same subject may contain variations in resolution

VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies

April 22, 2013
Author(s)
Yooyoung Lee, Ross J. Micheals, James J. Filliben, P J. Phillips
The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body

Dictionary Learning from Ambiguously Labeled Data

April 9, 2013
Author(s)
P J. Phillips, Yi-Chen Chen, Vishal M. Patel, Jaishanker K. Pillai, Rama Chellappa
We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label. The dictionary learning problem is solved using an iterative

Video-based Face Recognition via Joint Sparse Representation

January 2, 2013
Author(s)
P J. Phillips, Vishal M. Patel, Yi-Chen Chen, Rama Chellappa
In video-based face recognition, a key challenge is in exploiting the extra information available in a video. In addition, different video sequences of the same subject may contain variations in resolution, illumination, pose, and facial expressions. These

Dictionary-based Face Recognition from Video

December 10, 2012
Author(s)
P J. Phillips, Yi-Chen Chen, Vishal M. Patel, Rama Chellappa
The main challenge in recognizing faces in video is effec- tively exploiting the multiple frames of a face and the accompanying dynamic signature. One prominent method is based on extracting joint appearance and behavioral features. A second method models

Preliminary Studies on the Good, the Bad, and the Ugly Face Recognition Challenge Problem

November 26, 2012
Author(s)
P J. Phillips, J. R. Beveridge, David Bolme, Bruce A. Draper, Yui M. Lui
Face recognition has made significant advances over the last twenty years. State-of-the-art algorithms push the performanceenvelope to near perfect recognition rates on many face databases. Recently, the Good, the Bad, and the Ugly (GBU) face challenge

Demographic Effects on Estimates of Automatic Face Recognition Performance

November 22, 2012
Author(s)
P J. Phillips, Alice J. O'Toole, 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

A Grassmann Manifold-based Domain Adaptation Approach

November 20, 2012
Author(s)
P J. Phillips, Jingjing Zheng, Ming-Yu Liu, Rama Chellappa
Domain adaptation algorithms that handle shifts in the distribution between training and testing data are receiving much attention in computer vision. Recently, a Grassmann manifold-based domain adaptation algorithm that models the domain shift using

The Good, the Bad, and the Ugly Face Challenge Problem

November 20, 2012
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

Comparing Face Recognition Algorithms to Humans on Challenging Tasks

October 17, 2012
Author(s)
P J. Phillips, Alice O'Toole, Xiaobo An, Joseph Dunlop, Vaidehi Natu
We compared face identifcation by humans and machines using images taken under a variety of uncontrolled illumination conditions in both indoor and outdoor settings. Natural variations in a person's day-to-day appearance (e.g., hair style, facial

Cross-View Action Recognition via a Transferable Dictionary Pair

September 12, 2012
Author(s)
P J. Phillips, Jingjing Zheng, Zhuolin Jiang, Rama Chellappa
Discriminative appearance features are effective for recognizing actions in a fixed view, but generalize poorly to changes in viewpoint. We present a method for view- invariant action recognition based on sparse representations using a transferable dictio-

Ocular and Iris Recognition Baseline Algorithm

November 7, 2011
Author(s)
Yooyoung Lee, Ross J. Micheals, James J. Filliben, P J. Phillips, Hassan A. Sahibzada
Due to its distinctiveness, the human eye is a popular biometricv feature used to identity a person with high accuracy. The Grand Challenge in biometrics is to have an effective algorithm for subject verification or identification under a broad range of

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.