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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 101 - 125 of 129

Subspace Approximation of Face Recognition Algorithms: An Empirical Study

May 12, 2008
Author(s)
P J. Phillips, Pranab Mohanty, Sudeep Sarkar, Rangachar Kasturi
We present a theory for constructing linear subspace approximations to face recognition algorithms and empirically demonstrate that a surprisingly diverse set of face recognition approaches can be approximated well using a linear model. A linear model

Meta-Analysis of Third-Party Evaluations of Iris Recognition

August 24, 2007
Author(s)
Elaine M. Newton, P J. Phillips
Iris recognition has long been widely regarded as a highly accurate biometric, despite the lack of independent, large-scale testing of its performance. Recently, however, three third-party evaluations of iris recognition were performed. This paper compares

Face Recognition Algorithms surpass humans matching faces across changes in illumination

May 15, 2007
Author(s)
P. Jonathon Phillips, Alice J. O'Toole, Fang Jian, Julianne Ayadd, Nils Penard, Herve Abdi
We compared the accuracy of eight state-of-the-art face recognition algorithms with human performance on the same task. Humans and algorithms determined whether two face images, taken under different illumination conditions, were pictures of the same

Face Recognition Vendor Test 2006 and Iris Challenge Evaluation 2006 Large-Scale Results

March 29, 2007
Author(s)
P J. Phillips, K W. Bowyer, P J. Flynn, Alice J. O'Toole, W T. Scruggs, Cathy L. Schott, Matthew Sharpe
The Face Recognition Vendor Test (FRVT) 2006 and Iris Challenge Evaluation (ICE) 2006 are independent U.S. Government evaluations of face and iris recognition performance. These evaluations were conducted simultaneously at NIST using the same test

Comment on the CASIA v1 Iris Dataset

March 26, 2007
Author(s)
P J. Phillips, K W. Bowyer, P J. Flynn
The paper by Ma et al. [1] made a number of contributions to iris recognition including a novel iris recognition algorithm, a benchmark of standard approaches to iris recognition, and the establishment of an iris data set. The data set, Chinese Academy of

Preliminary Face Recognition Grand Challenge Results

February 15, 2006
Author(s)
P J. Phillips, P J. Flynn, W T. Scruggs, K W. Bowyer, W Worek
The goal of the Face Recognition Grand Challenge (FRGC) is to improve the performance of face recognition algorithms by an order of magnitude over the best results in Face Recognition Vendor Test (FRVT) 2002. The FRGC is designed to achieve this

Face Recognition Based on Frontal Views Generated from Non-Frontal Images

October 1, 2005
Author(s)
V Blanz, Patrick Grother, P. Jonathon Phillips, T Vetter
This paper presents a method for face recognition across large changes in viewpoint. Our method is based on a Morphable Model of 3D faces that represents face-specific information extracted from a dataset of 3D scans. For non-frontal face recognition in 2D

Overview of the Face Recognition Grand Challenge

October 1, 2005
Author(s)
P J. Phillips, P J. Flynn, W T. Scruggs, K W. Bowyer, J S. Chang, K Hoffman, J Marques, J Min, W Worek
Over the last couple of years, face recognition researchers have been developing new techniques, such as recognition from three-dimensional and high resolution imagery. These developments are being fueled by advances in computer vision techniques, computer

Linear and Generalized Linear Models for Analyzing Face Recognition Performance

August 17, 2005
Author(s)
J. R. Beveridge, Geof H. Givens, Bruce A. Draper, P. Jonathon Phillips
This paper introduces linear models (LM), generalized linear models (GLM), and generalized linear mixed models (GLMM) for analyzing performance of face recognition algorithms. These three statistical techniques are applied to analyzing the affect of

The NIST Human ID Evaluation Framework

April 1, 2003
Author(s)
Ross J. Micheals, Patrick J. Grother, P J. Phillips
In this paper, we investigate the utility of static anthropometric distances as a biometric for human identification. The 3D landmark data from the CAESAR database is used to form a simple biometric consisting of distances between fixed rigidly connected

Face Recognition Vendor Test 2002 Performance Metrics

March 1, 2003
Author(s)
Patrick Grother, Ross J. Micheals, P. Jonathon Phillips
We present the methodology and recognition performance characteristics used in the Face Recognition Vendor Test 2002. We refine the notion of a biometric imposter, and show that the traditional measures of identification and verification performance are

Face Recognition Vendor Test 2002: Evaluation Report

March 1, 2003
Author(s)
P J. Phillips, Patrick J. Grother, Ross J. Micheals, D M. Blackburn, Elham Tabassi, M Bone
The Face Recognition Vendor Test (FRVT) 2002 is an independently administered technology evaluation of mature face recognition systems. FRVT 2002 provides performance measures for assessing the capability of face recognition systems to meet requirement for

Dependence Characteristics of Face Recognition Algorithms

January 1, 2002
Author(s)
Andrew L. Rukhin, Patrick J. Grother, P J. Phillips, Stefan D. Leigh, E M. Newton, Nathanael A. Heckert
Nonparametric statistics for quantifying dependence between the output rankings of face recognition algorithms are described. Analysis of the archived results of a large face recognition study shows that even the better algorithms exhibit significantly

Transformation, Ranking, and Clustering for Face Recognition Algorithm Performance

January 1, 2002
Author(s)
Stefan D. Leigh, Nathanael A. Heckert, Andrew L. Rukhin, P J. Phillips, Patrick J. Grother, E M. Newton, M Moody, K Kniskern, S Heath
The performance of face recognition algorithms is recently of increased interest, although to date empirical analyses of algorithms have been limited to rank-based scores such a cumulative match score and receiver operating characteristic. This paper

Meta-Analysis of Face Recognition Algorithms

March 1, 2001
Author(s)
P J. Phillips, E M. Newton
To obtain a quantitative assessment of the state of automatic face recognition, we performed a meta-analysis of performance results of face recognition algorithms in the literature. The analysis was conducted on 24 papers that report identification

Computational and Performance Aspects of PCA-Based Face Recognition Algorithms

January 1, 2001
Author(s)
H Moon, P. Jonathon Phillips
Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the psychological and algorithmic face recognition literature. PCA is a statistical technique and its incorporation into a face recognition algorithm

The FERET Evaluation Methodology for Face-Recognition Algorithms

October 1, 2000
Author(s)
P J. Phillips, H Moon, S A. Rizvi, P J. Rauss
Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The face Recognition Technology (FERET) program has addressed both issues

Introduction to Evaluating Biometric Systems

February 21, 2000
Author(s)
P J. Phillips, Alvin F. Martin, Charles L. Wilson, Mark A. Przybocki
Biometric technology has the potential to provide secure access using user characteristics, biometric signatures, that cannot be lost, stolen, or easily duplicated. However, the actual performance of many existing systems is unknown. Potential users of

An Introduction to Evaluating Biometric Systems

February 1, 2000
Author(s)
P J. Phillips, Alvin F. Martin, Charles L. Wilson, Mark A. Przybocki
How and where biometric systems are deployed will depend on their performance. Knowing what to ask and how to decipher the answers can help you evaluate the performance of these emerging technologies. On the basis of media hype alone, you might conclude

On Performance Statistics for Biometric Systems

October 5, 1999
Author(s)
P J. Phillips
The major contribution of this paper is a duality between the identification and verification evaluation protocols. The duality (1) gives a mapping between identification and verification scores and (2) bounds identification and verification scores in

Assessing Algorithms as Computational Models for Human Face Recognition

June 1, 1999
Author(s)
P J. Phillips, Alice J. O'Toole, Y Cheng, B Ross, H A. Wild
We assessed the qualitative accord between several automatic face recognition algorithms and human perceivers. By comparing model- and human-generated measures of the similarity between pairs of faces, we were able to evaluate the suitability of the

Support Vector Machines Applied to Face Recognition

November 1, 1998
Author(s)
P J. Phillips
Face recognition is a K class problem, where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method. By reformulating the face recognition problem and re-interpreting the output of the SVM classifier, we