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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 101 - 121 of 121

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

The FERET Verification Testing Protocol for Face Recognition Algorithms

October 1, 1998
Author(s)
S A. Rizvi, P. Jonathon Phillips, H Moon
Two critical performance characterizations of biometric algorithms, including face recogntion, are identification and verification. In face recognition, FERET is the de facto standard evaluation methodology. Identification performance of face recognition

Matching Pursuit Filters Applied to Face Identification

August 1, 1998
Author(s)
P J. Phillips
We present a face identification algorithm that automatically processes an unknown image by locating and identifying the face. The heart of the algorithm is the use of pursuits filters. A matching pursuit filter is an adapted wavelet expansion, where the

Issues in Evaluating Face Recognition Algorithms

June 22, 1998
Author(s)
P J. Phillips, R. McCabe, R Chellappa
Biometric-based identification and verification systems are poised to become a key technology, with applications including controlling access to buildings and computers, reducing fraudulent transactions in electronic commerce, and discouraging illegal
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