Publications

Displaying 1 - 25 of 28

Face Analysis Technology Evaluation (FATE) Part 10: Performance of Passive, Software-based Presentation Attack Detection (PAD) Algorithms

September 19, 2023

Author(s)

Mei Lee Ngan, Patrick J. Grother, Austin Hom

This report quantifies the accuracy of passive purely software-based face presentation attack detection (PAD) algorithms operating on conventional 2D imagery of various presentation attack instruments (PAI). The algorithms were submitted to the

Face Analysis Technology Evaluation (FATE) Part 11: Face Image Quality Vector Assessment: Specific Image Defect Detection

September 19, 2023

Author(s)

Joyce Yang, Patrick J. Grother, Mei Lee Ngan, Kayee Hanaoka, Austin Hom

Ongoing Face Recognition Vendor Test (FRVT) Part 9a: Face Recognition Verification Accuracy on Distinguishing Twins

September 22, 2022

Author(s)

Kayee Hanaoka, Mei Lee Ngan, Patrick J. Grother, Austin Hom

Face Recognition Vendor Test (FRVT) Part 4A: MORPH - Utility of 1:N Face Recognition Algorithms for Morph Detection

July 29, 2022

Author(s)

Mei Lee Ngan, Patrick J. Grother, Kayee Hanaoka

This report is a part of a series of studies on the topic of face morphing, its relevance and implications as a vulnerability to automated face recognition, and methods to aid in detecting morphs. Expanding on concepts introduced in a study conducted by

Face Recognition Vendor Test (FRVT) Part 7: Identification for Paperless Travel and Immigration

July 13, 2021

Author(s)

Patrick J. Grother, Austin Hom, Mei Lee Ngan, Kayee Hanaoka

One-to-many biometric search systems are discussed in their role of positive and negative identification - the former refers to the expectation that person in a probe sample is present in the database (as in access to an office) while the latter presumes

Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face recognition accuracy with face masks using post-COVID-19 algorithms

November 30, 2020

Author(s)

Mei L. Ngan, Patrick J. Grother, Kayee K. Hanaoka

This is the second of a series of reports on the performance of face recognition algorithms on faces occluded by protective face masks commonly worn to reduce inhalation and exhalation of viruses. This is a continuous study is being run under the Ongoing

Ongoing Face Recognition Vendor Test (FRVT) Part 6A: Face recognition accuracy with masks using pre- COVID-19 algorithms

July 24, 2020

Author(s)

Mei L. Ngan, Patrick J. Grother, Kayee K. Hanaoka

This is the first of a series of reports on the performance of face recognition algorithms on faces occluded by masks, run under the Ongoing Face Recognition Vendor Test (FRVT) executed by the National Institute of Standards and Technology (NIST). This

Face Recognition Vendor Test (FRVT) Part 4: MORPH - Performance of Automated Face Morph Detection

March 6, 2020

Author(s)

Mei L. Ngan, Patrick J. Grother, Kayee K. Hanaoka, Jason M. Kuo

The FRVT MORPH test provides ongoing independent testing of prototype face morphing attack detection (MAD) technologies. The evaluation is designed to obtain commonly measured assessment of morph detection capability to inform developers and current and

Face Recognition Vendor Test Part 3: Demographic Effects

December 19, 2019

Author(s)

Patrick J. Grother, Mei L. Ngan, Kayee K. Hanaoka

Face Recognition Vendor Test (FRVT) Part 2: Identification

September 13, 2019

Author(s)

Patrick Grother, Mei Ngan, Kayee Hanaoka

This report updates and extends NIST Interagency Report 8238, documenting performance of new face recognition algorithms submitted for evaluation to NIST in November 2018. The algorithms implement one-to-many identification of faces appearing in two-

Ongoing Face Recognition Vendor Test (FRVT) Part 2: Identification

November 27, 2018

Author(s)

Patrick J. Grother, Mei L. Ngan, Kayee K. Hanaoka

This report documents performance of face recognition algorithms applied to the one-to-many identification of faces appearing in portrait images. The primary dataset is comprised of 26.6 million reasonably well controlled live photos of 12.3 million

Tattoo Recognition Technology - Evaluation (Tatt-E): Performance of Tattoo Identification Algorithms

October 31, 2018

Author(s)

Mei L. Ngan, Patrick J. Grother, Kayee K. Hanaoka

NIST performed a large scale empirical evaluation of tattoo recognition algorithms. The test leveraged large operational datasets comprised of tattoo images from law enforcement databases, enabling evaluation with enrollment database sizes of up to 100,000

The Text Recognition Algorithm Independent Evaluation (TRAIT)

December 15, 2017

Author(s)

Afzal A. Godil, Patrick J. Grother, Mei L. Ngan

The report describes and presents the results for text detection and recognition (TRAIT) evaluation in support of forensic investigations of digital media. These im- ages are of interest to NISTs partner law enforcement agencies that seek to employ text

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

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