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Information Technology

Biometrics

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  • Published Date
Displaying 226 - 250 of 412

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

WSQ Problem with Two-Thumb Captures from Large Platen Live-Scan Devices

November 18, 2010
Author(s)
Craig I. Watson
This paper investigates an issue with the Wavelet Scalar Quantization (WSQ) compression algorithm which causes severe degradation of the compressed image. The problem was first noticed when compressing the two-thumb images from live-scan identification

SlapSegII Analysis: Matching Segmented Fingerprint Images

November 16, 2010
Author(s)
Craig I. Watson
This report is an extension of the SlapSegII evaluation and will study the affect of slap fingerprint segmentation on matching accuracy. This study will compare matching accuracy of hand marked segmentation data with the automated segmentation algorithms

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

Image specific error rate: A biometric performance metric

August 20, 2010
Author(s)
Elham Tabassi
Image-specific false match and false non-match error rates are defined by inheriting concepts from the biometric zoo. These metrics support failure mode analyses by allowing association of a covariate (e.g., dilation for iris recognition) with a matching

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

Multiple Encounter Dataset I (MEDS-I)

May 9, 2010
Author(s)
Craig I. Watson
In December, 2008, the FBI provided MITRE with an extract of submissions of deceased persons. The submissions contain face images of subjects with multiple encounters over time. The type 10 records (face and SMT) are mostly frontal or near frontal face

Significance Test in Operational ROC Analysis

April 5, 2010
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker, Robert C. Hagwood
To evaluate the performance of fingerprint-image matching algorithms on large datasets, a receiver operating characteristic (ROC) curve is applied. From the operational perspective, the true accept rate (TAR) of the genuine scores at a specified false

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

Biometrics Systems Include Users

December 16, 2009
Author(s)
Mary F. Theofanos, Ross J. Micheals, Brian C. Stanton
Where do biometrics come from? The “canonical” standard (Wayman) biometric system model includes the biometric presentation and a biometric sensor but not the user themselves. Having this model facilitates having shared vocabulary and abstraction for

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
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