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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 176 - 200 of 242

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

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