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Search Publications by: Mei Lee Ngan (Fed)

Displaying 26 - 50 of 53

The 2017 IARPA Face Recognition Prize Challenge (FRPC)

November 27, 2017

Author(s)

Patrick J. Grother, Mei L. Ngan, Lars Ericson, Kayee K. Hanaoka, Christopher Boehnen

This report documents NISTs execution of the Intelligence Advanced Research Projects Activity (IARPA) Face Recognition Prize Challenge 2017. The (FRPC) was conducted to assess the capability of contemporary face recognition algorithms to recognize faces

Face In Video Evaluation (FIVE) Face Recognition of Non-Cooperative Subjects

March 6, 2017

Author(s)

Patrick J. Grother, Mei L. Ngan, George W. Quinn

NISTIR 8078 - Tattoo Recognition Technology - Challenge (Tatt-C) - Outcomes and Recommendations

September 8, 2016

Author(s)

Mei L. Ngan, George W. Quinn, Patrick J. Grother

NISTIR 8109 - Tattoo Recognition Technology - Best Practices (Tatt-BP) - Guidelines for Tattoo Image Collection

September 6, 2016

Author(s)

Mei L. Ngan, George W. Quinn, Patrick J. Grother

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 - Best Practices (Tatt-BP): Guidelines for Tattoo Image Collection

February 25, 2016

Author(s)

Mei L. Ngan, George W. Quinn, Patrick J. Grother

This document, along with a supplementary poster and a set of instructional slides, provides best practice guidelines for the collection of good quality tattoo images. As an outcome of the Tatt-C 2015 activity, algorithm failure to correctly match a tattoo

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

Face Recognition Vendor Test (FRVT) - Performance of Automated Gender Classification Algorithms

April 20, 2015

Author(s)

Mei L. Ngan, Patrick J. Grother

Facial gender classification is an area studied in the Face Recognition Vendor Test (FRVT) with Still Facial Images Track. While peripheral to automated face recognition, it has become a growing area of research, given its potential use in various

Tattoo Recognition Technology - Challenge (Tatt-C): An Open Tattoo Database for Developing Tattoo Recognition Research

March 31, 2015

Author(s)

Mei L. Ngan, Patrick J. Grother

Tattoos have been used for many years to assist law enforcement in investigations leading to the identification of criminals and victims. While tattoos lack the distinctiveness and permanence characteristics of a primary biometric trait, it is an elective

Face Recognition Vendor Test (FRVT) Performance of Face Identification Algorithms NIST IR 8009

May 20, 2014

Author(s)

Patrick J. Grother, Mei Lee Ngan

This report documents performance of one-to-many face identification algorithms, and compares it with that in 2010. Performance in this context refers recognition accuracy and computational resource usage as measured by executing those algorithms on

Face Recognition Vendor Test (FRVT) - Performance of Automated Age Estimation Algorithms

March 20, 2014

Author(s)

Mei L. Ngan, Patrick J. Grother

Facial Age Estimation is an area of study new to the Face Recognition Vendor Test (FRVT) Still Track. While peripheral to traditional face recognition, it has become a growing area of research given its potential use in commercial and biometric

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