NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: Yooyoung Lee (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 26 - 45 of 45

2018 MediFor Challenge

July 23, 2019
Author(s)
Jonathan G. Fiscus, Haiying Guan, Andrew Delgado, Timothee N. Kheyrkhah, Yooyoung Lee, Daniel F. Zhou, Amy Yates
Media forensics is the science and practice of determining the authenticity and establishing the integrity of audio and visual media. DARPA's Media Forensics (MediFor) program brings together world-class researchers to develop technologies for the

Manipulation Data Collection and Annotation Tool for Media Forensics

June 17, 2019
Author(s)
Eric Robertson, Haiying Guan, Mark Kozak, Yooyoung Lee, Amy Yates, Andrew Delgado, Daniel F. Zhou, Timothee N. Kheyrkhah, Jeff Smith, Jonathan G. Fiscus
With the increasing diversity and complexity of media forensics techniques, the evaluation of state-of-the-art detectors are impeded by lacking the metadata and manipulation history ground-truth. This paper presents a novel image/video manipulation

MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation

January 11, 2019
Author(s)
Haiying Guan, Mark Kozak, Eric Robertson, Yooyoung Lee, Amy Yates, Andrew Delgado, Daniel F. Zhou, Timothee N. Kheyrkhah, Jeff Smith, Jonathan G. Fiscus
We provide a benchmark for digital media forensic challenge evaluations. A series of datasets are used to assess the progress and deeply analyze the performance of diverse systems on different media forensic tasks across last two years. The benchmark data

MediFor Nimble Challenge Evaluation 2017

August 23, 2017
Author(s)
Jonathan G. Fiscus, Haiying Guan, Yooyoung Lee, Amy Yates, Andrew Delgado, Daniel F. Zhou, David M. Joy, August L. Pereira
NIST presentation slides for DARPA MediFor Program One-Year PI Meeting

MediFor Nimble Challenge Evaluation

April 17, 2017
Author(s)
Jonathan G. Fiscus, Haiying Guan, Yooyoung Lee, Amy Yates, Andrew Delgado, Daniel F. Zhou, Timothee N. Kheyrkhah

Generalizing Face Quality and Factor Measures to Video

September 23, 2014
Author(s)
Yooyoung Lee, P. Jonathon Phillips, James Filliben, J. R. Beveridge, Hao H. Zhang
Methods for assessing the impact of factors and image-quality metrics for still face images are well-understood. The extension of these factors and quality measures to faces in video has not, however, been explored. We present a specific methodology for

Identifying Face Quality and Factor Measures for Video

May 20, 2014
Author(s)
Yooyoung Lee, P. Jonathon Phillips, James Filliben, J. R. Beveridge, Hao Zhang
This paper identifies important factors for face recognition algorithm performance in video. The goal of this study is to understand key factors that affect algorithm performance and to characterize the algorithm performance. We evaluate four factor

VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies

April 22, 2013
Author(s)
Yooyoung Lee, Ross J. Micheals, James J. Filliben, P J. Phillips
The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body

Ocular and Iris Recognition Baseline Algorithm

November 7, 2011
Author(s)
Yooyoung Lee, Ross J. Micheals, James J. Filliben, P J. Phillips, Hassan A. Sahibzada
Due to its distinctiveness, the human eye is a popular biometricv feature used to identity a person with high accuracy. The Grand Challenge in biometrics is to have an effective algorithm for subject verification or identification under a broad range of

Robust Iris Recognition Baseline for the Grand Challenge

May 17, 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

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

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

An Automated Video-Based System For Iris Recognition

June 2, 2009
Author(s)
Yooyoung Lee, P. Jonathon Phillips, Ross J. Micheals
We have successfully implemented a Video-based Automated System for Iris Recognition (VASIR), evaluating its successful performance on the MBGC dataset. The proposed method facilitates the ultimate goal of automatically detecting an eye area, extracting

Conformance Test Suite for CBEFF Biometric Information Records

September 29, 2007
Author(s)
Yooyoung Lee, Fernando L. Podio, Mark Jerde
Deployment of standards-based biometric technologies is expected to significantly raise levels of security for critical infrastructures that has not been possible to date with other technologies. These systems require a comprehensive set of technically
Was this page helpful?