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Search Publications by: Elham Tabassi (Fed)

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Displaying 1 - 25 of 37

A Plan for Global Engagement on AI Standards

July 26, 2024
Author(s)
Jesse Dunietz, Elham Tabassi, Mark Latonero, Kamie Roberts
Recognizing the importance of technical standards in shaping development and use of Artificial Intelligence (AI), the President's October 2023 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (EO 14110)

Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

July 26, 2024
Author(s)
Chloe Autio, Reva Schwartz, Jesse Dunietz, Shomik Jain, Martin Stanley, Elham Tabassi, Patrick Hall, Kamie Roberts
This document is a cross-sectoral profile of and companion resource for the AI Risk Management Framework (AI RMF 1.0) for Generative AI, pursuant to President Biden's Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Artificial Intelligence. The

American Competitiveness Of a More Productive Emerging Tech Economy Act (The American COMPETE Act)

August 11, 2023
Author(s)
Commerce Secretary, Kevin A. Kimball, Matthew Hoehler, Anne Lane, Elham Tabassi, Connie LaSalle, Mark VanLandingham, James A. Warren, Naomi Lefkovitz, Nada T. Golmie, Chris Greer, Matthew Scholl, Dylan Yaga, Andrew C. Wilson, Kevin Stine, Karen Reczek, Institute for Defense Analyses Science and Technology Policy Institute (IDA STPI), Quantum Economic Development Consortium (QED-C), Federal Trade Commission (FTC)
Under DIVISION FF, Title XV, §1501 of the Consolidated Appropriations Act of 2021 (Public Law 116-260)—the "American Competitiveness Of a More Productive Emerging Tech Economy Act" (the "American COMPETE Act")—the United States Congress directed the

Artificial Intelligence Risk Management Framework (AI RMF 1.0)

January 26, 2023
Author(s)
Elham Tabassi
As directed by the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI and

NIST Fingerprint Image Quality 2

July 13, 2021
Author(s)
Elham Tabassi, Martin Olsen, Oliver Bausinger, Christoph Busch, Andrew Figlarz, Gregory Fiumara, Olaf Henniger, Johannes Merkle, Timo Ruhland, Christopher Schiel, Michael Schwaiger
NIST Fingerprint Image Quality (NFIQ 2) is open source software that links image quality of optical and ink 500 pixel per inch fingerprints to operational recognition performance. This allows quality values to be tightly defined and then numerically

NIST Special Database 302: Nail to Nail Fingerprint Challenge

December 11, 2019
Author(s)
Gregory P. Fiumara, Patricia A. Flanagan, John D. Grantham, Kenneth Ko, Karen Marshall, Matthew Schwarz, Elham Tabassi, Bryan Woodgate, Christopher Boehnen
In September 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a data collection as part of its Nail to Nail (N2N) Fingerprint Challenge. Participating Challengers deployed devices designed to collect an image of the full nail to nail

Nail to Nail Fingerprint Challenge: Enrollment Set Size Variability

June 24, 2019
Author(s)
Gregory P. Fiumara, Kenneth Ko, Elham Tabassi, Patricia A. Flanagan, John D. Grantham, Karen Marshall, Matthew Schwarz, Bryan Woodgate
In September 2017, the Intelligence Advanced Research Projects Activity held a fingerprint data collection as part of the Nail to Nail Fingerprint Challenge. Thousands of latent fingerprint images collected at the Challenge were searched against rolled

NIST Special Database 301: Nail to Nail Fingerprint Challenge Dry Run

July 11, 2018
Author(s)
Gregory P. Fiumara, Patricia A. Flanagan, Matthew Schwarz, Elham Tabassi, Christopher Boehnen
In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection

Nail to Nail Fingerprint Challenge: Prize Analysis

May 3, 2018
Author(s)
Gregory P. Fiumara, Elham Tabassi, Patricia A. Flanagan, John D. Grantham, Kenneth Ko, Karen Marshall, Matthew Schwarz, Bryan Woodgate, Christopher Boehnen
In September 2017, the Intelligence Advanced Research Projects Activity held a fingerprint data collection as part of the Nail to Nail Fingerprint Challenge. Participating Challengers deployed devices designed to collect an image of the full nail to nail

Latent Fingerprint Value Prediction: Crowd-based Learning

December 31, 2017
Author(s)
Elham Tabassi, Anil K. Jain, Tarang Chugh, Kai Cao, Jiayu Zhou
Latent fingerprints are one of the most crucial sources of evidence in forensic investigations. As such, devel- opment of automatic latent fingerprint recognition systems to quickly and accurately identify the suspects is one of the most pressing problems

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].

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

Fingerprint Vendor Technology Evaluation

January 8, 2015
Author(s)
Craig I. Watson, Gregory P. Fiumara, Elham Tabassi, Su L. Cheng, Patricia A. Flanagan, Wayne J. Salamon
FpVTE was conducted primarily to assess the current capabilities of fingerprint matching algorithms using operational datasets containing several million subjects. There were three classes of participation that examined various finger combinations from

Performance evaluation of fingerprint open-set identification algorithms

September 22, 2014
Author(s)
Elham Tabassi, Craig I. Watson, Gregory P. Fiumara
We report performance of one-to-many fingerprint identification algorithms using one, two, four, eight or ten fingers for recognition. Performance is quantified in terms of recognition accuracy (false positive and false negative identification rate)

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 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

Iris Quality Calibration and Evaluation (IQCE): Evaluation Report

September 24, 2011
Author(s)
Elham Tabassi, Patrick J. Grother, Wayne J. Salamon
Iris is rapidly gaining acceptance and support as a viable biometric. Several large scale identity management applications are either using or considering iris as their secondary or primary biometric for verification. While there are several academic

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

IREX I: Performance of Iris Recognition Algorithms on Standard Images

October 30, 2009
Author(s)
Patrick J. Grother, Elham Tabassi, George W. Quinn, Wayne J. Salamon
The IREX program supports the development of interoperable iris imagery for use in high performance biometric applications. The IREX evaluation, was conducted in cooperation with the iris recognition industry to demonstrate that standardized image formats