Performance of biometric systems is dependent on the quality of the acquired input samples. If quality can be improved, either by sensor design, by user interface design, or by standards compliance, better performance can be realized. For those aspects of quality that cannot be designed-in, an ability to analyze the quality of a live sample is needed. This is useful primarily in initiating the reacquisition from a user, but also for the real-time selection of the best sample, and the selective invocation of different processing methods. It is the key component in quality assurance management, and because quality algorithms often embed the same image (or signal) analyses needed to assess conformance to underlying data interchange standards, they can be used in automated image screening applications.
Quality analysis is a technical challenge because it is most helpful when the measures reflect the performance sensitivities of one or more target biometric matchers. NIST addressed this problem in August 2004 when it issued the NIST Fingerprint Image Quality algorithm, which was designed to be predictive of the performance of minutiae matchers. Since then NIST has been considering how quality measures should be evaluated, developing quality measures for other biometrics, and considering the wider use of such measures. In addition NIST is active in the new SC37 and M1 standardization activities on biometric quality and sample conformance.
NFIQ was developed in 2004 to produce a quality value from a fingerprint image that is directly predictive of expected matching performance. With advances in fingerprint technology since 2004, an update to NFIQ is needed. A workshop was held in March 2010 at NIST to address the technical status of fingerprint quality assessment technology, and to engage industry to improve core finger image quality assessment technology based on lessons learned from recent deployments of quality assessment algorithms (including NFIQ) in large-scale identity management applications. Options for the future of NFIQ were discussed and the community overwhelmingly recommended a new (open source) version of NFIQ to be developed in consultation and collaboration with users and industry. To that end, National Institute of Standards and Technology (NIST) and Bundesamt für Sicherheit in der Informationstechnik (BSI) in Germany have teamed up to develop the new and improved open source NIST Finger Image Quality (NFQ 2.0) and extend invitation to research organizations and industry members to provide specific support in the development of NFIQ 2.0. Please see the call for participation for more detail. Please send your suggestions and/or comments to nfiq2.development [at] nist.gov .
March 2-4, 2010
The conference aimed to identify the important and new performance metrics and to expose best practice for evaluation. New performance results are not themselves in scope - instead the intention was to capture recent and best practice, to contrast that with the past, and to expose what is needed in the future. The overarching goal was to refine the concept of biometric performance and to ultimately elevate adoption and effectiveness of biometric technologies.
November 7-8, 2007
The workshop aimed at improving accuracy of biometric systems by incorporating quality assessment technologies into the sample acquisition process. It aimed to assess current quality measurement capabilities and to identify technologies, factors, operational paradigms, and standards that can measurably improve quality.
March 8-9, 2006
The workshop aimed at improving performance of biometric systems. It aimed to assess current quality measurement capabilities and to identify technologies, factors, operational paradigms, and standards that can measurably improve quality.
Quality summarization NISTIR 7422 provides technical guidance for users of biometric quality algorithms in large-scale enterprise operations. Specifically, it recommends the computation and use of performance-related quality summaries, which, for verification, should serve as measures of the overall expected false non-match rate.
NIST released the NIST Fingerprint Image Quality algorithm. Its key innovation is to produce a quality value from a fingerprint image that is directly predictive of expected matching performance. Source code for the algorithm is included in the NBIS distribution.