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Displaying 26 - 50 of 64

Documentation for ROC Baseline 2016

July 13, 2016
Author(s)
James R. Matey, Su L. Cheng, Patrick J. Grother, Mei L. Ngan, George W. Quinn, Elham Tabassi, Craig I. Watson
We present ROC baseline data to support the recommendations in Matey et al [6].

Tattoo Recognition Technology - Challenge (Tatt-C): Outcomes and Recommendations

September 15, 2015
Author(s)
Mei L. Ngan, George W. Quinn, Patrick J. Grother
Tattoos have been used for many years to assist law enforcement in investigations leading to the identification of both criminals and victims. A tattoo is an elective biometric trait that contains additional discriminative information to support person

Modest proposals for improving biometric recognition papers

August 31, 2015
Author(s)
James R. Matey, George W. Quinn, Patrick J. Grother, Elham Tabassi, Craig I. Watson, James L. Wayman
We present practical recommendations for improving the clarity, transparency, and usefulness of many biometric papers. Several of the recommendations can be enabled by preparing a publicly available library of state of the art Receiver Operating

IREX V: Guidance for Iris Image Collection

June 12, 2014
Author(s)
George W. Quinn, JAmes Matey, Elham Tabassi, Patrick J. Grother
This document provides guidance for the proper collection of iris images. Problems that occur during image acquisition can lead to poor quality samples. If the subject was looking down or blinking at the moment of capture, the image should be rejected and

IREX IV: Part 2 Compression Profiles for Iris Image Compression

January 23, 2014
Author(s)
George Quinn, Patrick Grother, Mei Ngan, Nick Rymer
The IREX IV evaluation builds upon IREX III as a performance test of one-to-many iris recognition. This report is the second part of the IREX IV evaluation, which specifically, evaluates the ability of automated iris recognition algorithms to match heavily

IREX IV: Part 1, Evaluation of Iris Identification Algorithms

July 11, 2013
Author(s)
George W. Quinn, Patrick J. Grother, Mei L. Ngan, James R. Matey
IREX IV aims to provide a fair and balanced scientific evaluation of the performance of automated iris recognition algorithms. IREX IV evaluated the performance of 66 identification (i.e. one-to-many matching) algorithms submitted by 12 companies and

IREX VI - Temporal Stability of Iris Recognition Accuracy

July 11, 2013
Author(s)
Patrick J. Grother, James R. Matey, Elham Tabassi, George W. Quinn, Michael Chumakov
Background: Stability is a required definitional property for a biometric to be useful. Quantitative statements of stability are operationally important as they dictate re-enrollment schedules e.g. of a face on a passport. Ophthalmologists consider the

IREX III Supplement 1: Failure Analysis

April 18, 2012
Author(s)
George W. Quinn, Patrick J. Grother
Iris recognition has the potential to be extremely accurate, but it is highly dependent on the quality of the input data. Iris occlusion, off-axis gaze, blurred images, and iris rotation are common problems that can make recognizing individuals more

IREX III - Performance of Iris Identification Algorithms

April 3, 2012
Author(s)
Patrick J. Grother, George W. Quinn, James R. Matey, Mei L. Ngan, Wayne J. Salamon, Gregory P. Fiumara, Craig I. Watson
Iris recognition has long been held as an accurate and fast biometric. In the first public evaluation of one-to-many iris identification technologies, this third activity in the Iris Exchange (IREX) program has measured the core algorithmic efficacy and