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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 ISO/IEC JTC1 SC37 standardization activities on biometric quality and sample conformance (ISO/IEC 29794).

Upcoming Workshops

  • 16-18 November 2021: Workshop on Face Image Quality
    • 17 June 2021: Iris Experts Group Meeting (2021)
      • The IEG is a forum for the discussion of technical questions of interest to US Government (USG) agencies and their staff that are employing or may employ iris recognition to carry out their mission.  Members include subject matter experts from USG agencies, academia and the commercial world. The meeting agenda is based on input from the members and is under development. The workshop is virtual, free, and open to the public, but requires pre-registration.

    Active Tasks

    Face: FRVT Quality Assessment

    Face recognition accuracy has improved markedly due to development of new recognition algorithms and approaches. Nevertheless, recognition error rates remain significantly above zero, particularly in applications where photography of faces is difficult or when stringent thresholds must be applied to recognition outcomes to reduce false positives. For those applications that retain an image as an authoritative reference sample against which future recognitions are done, it is critical to maintain database quality. While standards exist for interchange of face images, and those standards additionally regulate the capture of images, there are no standards for how face image quality must be assessed nor are there performance evaluations for automated quality assessment algorithms.

    Fingerprint: NFIQ 2

    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 teamed up to develop the new and improved open source NIST Finger Image Quality (NFIQ 2).

    Iris: Iris Experts Group

    The Iris Experts Group (IEG) is a forum for the discussion of technical questions of interest to USG agencies and their staff that are employing or may employ iris recognition to carry out their mission. Quality of iris capture is an important aspect discussed by the IEG.

    Other Tasks

    Workshop Archive

    Group ARchive

    Software/Publication Archive

    Created June 8, 2010, Updated June 16, 2021