Elham Tabassi, an electronics engineer in the Information Access Division, received the American National Standards Institute (ANSI) 2012 Next Generation Award. The award "honors individuals who have been engaged in standardization or conformity assessment activities for less than eight years and who have, during this time, demonstrated vision, leadership, dedication, and significant contributions to their chosen field of activity."
By Shahram Orandi, John M. Libert, John G. Granthan, Kenneth Ko, Stephen S. Wood, Jin Chu Wu, Lindsay M. Petersen, and Bruce Bandini
This paper presents the findings of a study initially conducted to measure the operational impact of JPEG 2000 lossy compression on 1000 ppi fingerprint imagery at various levels of compression, but later expanded to include lossless compression. The study examines several such compression algorithms and compares them using criteria used to measure the effectiveness of the compression algorithm as well as its throughput using actual fingerprint imagery.
John M. Libert, Shahram Orandi, and John G. Grantham
This paper presents the findings of a study conducted to compare the effects of WSQ and JPEG 2000 compression on 500 ppi fingerprint imagery at a typical operational compression rate of 0.55 bpp (bits per pixel), corresponding to an effective compression ratio of approximately 15:1. Compression effects are measured using peak signal to noise ratio (PSNR), proportion of pixels changed via compression regardless of magnitude, and a frequency analytic method the Spectral Image Validation/Verification (SIVV) metric.
By George W. Quinn and Patrick Grother
Iris recognition has the potential to be extremely accurate, but it is highly dependent on the quality of the input data. The purpose of this failure analysis is to identify the causes of poor sample quality in the dataset and to provide best practice recommendations for how to improve the quality of captured samples.
By Yooyoung Lee, James J. Filliben, Ross J. Michaels, and P. Jonathon Phillips
This paper introduces an effective and structured methodology for carrying out a biometric system sensitivity analysis. The goal of sensitivity analysis is to provide the researcher/developer with the insight and understanding of the key factors that affect the matching performance of the biometric system under study.
By Svetlana Z. Lowry, Matthew T. Quinn, Mala Ramaiah, David Brick, Emily S. Patterson, Jiajie Zhang, Patricia Abbott, and Michael C. Gibbons
This report details recommendations to enhance HER usability when supporting pediatric patient care and identifies promising areas for EHR innovation. It also illustrates unique pediatric considerations in the context of representative clinical scenarios.
By Emile Morse, Mary Theofanos, Yee-Yin Choong, Celeste Paul, Aiping Zhang, and Hannah Wald
This paper presents the findings of a NIST PIV usability pilot study. It presents recommendations to improve the usability of PIV smartcard implementations, particularly within the federal government, where Homeland Security Presidential Directive-12 mandates smartcard use.
By Ross Michaels, Kevin Mangold, Matthew Aronoff, Kayee Kwong, and Karen Marshall
NIST Special Publication 500-288
Web Services-Biometric Devices is a specification describing how to expose a biometric sensor to various clients via web services. By using Web services as a means for interoperability, the capabilities and reach of biometrics is significantly improved.
By Svetlana Z. Lowry, Matthew T. Quinn, Mala Ramaiah, Robert M. Schumacher, Emily S. Patterson, Robert North, Jiajie Zhang, Michael C. Gibbons, and Patricia Abbott
This document summarizes the rationale for an Electronic Health Record (EHR) Usability Protocol (EUP) and outlines procedures for design evaluation and human user performance testing of EHR systems. The procedures include general steps and guidance for evaluating an EHR user interface from clinical and hu-man factors perspectives, and for conducting a validation study (i.e., summative usability test) of EHR user interfaces with representative user groups performing realistic tasks.
By Patrick Grother, George Quinn, J.R. Matey, M. Ngan, Wayne Salamon, G. Fiumara, and Craig Watson
Iris recognition has long been held as an accurate and fast bio-metric. In the first public evaluation of one-to-many iris identification technologies, this third activity in the Iris Exchange (IREX) program measured the core algorithmic efficacy and duration of the core processing functions of 92 algorithms from 11 implementing organizations operating on nearly 6 million images of 4 million eyes of 2 million people. As such, this report documents the state of the art of iris recognition technology operating on archival imagery, demonstrates the existence of a large and di-verse range of implementations beyond those described in the academic literature, and identifies factors contributing to recognition failure.
By Yooyoung Lee, Ross Micheals, James Filliben, Jonathon Phil-lips, and Hassan Sahibzada
Due to its distinctiveness, the human eye is a popular biometric 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 image and environmental conditions. As a response to the challenge, this paper presents baseline performance results derived from an enhanced version of VASIR (Video-based Automated System for Iris Recognition), as well as initial performance results based on a broader ocular recognition system.
By George Quinn and Patrick Grother
This report provides a comprehensive assessment of the ability of face recognition algorithms to compare compressed standard face images. Six well-performing algorithms from the Multiple Biometric Evaluation (MBE) 2010 Still Face Track are used to compare face images compressed in JPEG and JPEG2000 for-mats. A primary goal is to identify the maximum storage constraints under which verification systems can effectively operate. Toward this end, we provide guidelines and recommendations for the efficient compression and storage of face images for bio-metric applications.