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Biometric Sample Quality

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Synonyms

Biometric quality evaluation; Performance of biometric quality measures

Definition

The intrinsic characteristic of a biometric signal may be used to determine its suitability for further processing by the biometric system or assess its conformance to preestablished standards. The quality score of a biometric sample signal is a scalar summary of the sample's quality.

Quality measurement algorithm is regarded as a black box that converts an input sample to an output scalar. Evaluation is done by quantifying the association between those values and observed matching results. For verification, these would be the false match and non-match rates. For identification, the matching results would usually be false match and nonmatch rates [1], but these may be augmented with rank and candidate-list length criteria. For a quality algorithm to be effective, an increase in false match and false nonmatch rates is expected as quality degrades.

Introduction

Biometric quality measurement...

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References

  1. Mansfield, A.J.: ISO/IEC 19795-1 Biometric Performance Testing and Reporting: Principles and Framework, FDIS ed., JTC1/ SC37/Working Group 5, August 2005, http://isotc.iso.org/isotcportal

  2. Ko, T., Krishnan, R.: Monitoring and reporting of fingerprint image quality and match accuracy for a large user application. In: Proceedings of the 33rd Applied Image Pattern Recognition Workshop. IEEE Computer Society, pp. 159–164 (2004)

    Google Scholar 

  3. Proceedings of the NIST Biometric Quality Workshop. NIST (March 2006), http://www.itl.nist.gov/iad/894.03/quality/workshop/presentations.html

  4. Benini, D., et al.: ISO/IEC 29794-1 Biometric Quality Framework Standard, 1st ed. JTC1/SC37/Working Group 3 (Jan 2006), http://isotc.iso.org/isotcportal

  5. Chen, Y., Dass, S., Jain, A.: Fingerprint quality indices for predicting authentication performance. In: Procedings of the Audio- and Video-Based Biometric Person Authentication (AVBPA), pp. 160–170 (July 2005)

    Google Scholar 

  6. Tabassi, E.: Fingerprint Image Quality, NFIQ, NISTIR 7151 ed., National Institute of Standards and Technology (2004)

    Google Scholar 

  7. Bioscrypt Inc., Systems and Methods with Identify Verification by Comparison and Interpretation of Skin Patterns Such as Fingerprints, http://www.bioscrypt.com (June 1999)

  8. Alonso-Fernandez, F., Fierrez-Aguilar, J., Ortega-Garcia, J., A review of schemes for fingerprint image quality computation. In COST 275 – Biometrics-based recognition of people over the internet (October 2005)

    Google Scholar 

  9. Lim, E., Jiang, X., Yau, W.: Fingerprint quality and validity analysis. In: Proceedings of the IEEE Conference on Image Processing, vol. 1, pp. 469–472 (September 2002)

    Google Scholar 

  10. Tilton, C., et al.: The BioAPI Specification, American National Standards Institute, Inc. (2002)

    Google Scholar 

  11. ISO/IEC JTC1/SC37/Working Group 3, ISO/IEC 19794 Biometric Data Interchange Formats, http://isotc.iso.org/isotcportal (2005)

  12. Tabassi, E.: A novel approach to fingerprint image quality. In: IEEE International Conference on Image Processing ICIP-05, Genoa, Italy (September 2005)

    Google Scholar 

  13. Chambers, J.M., Cleveland, W.S., Kleiner, B., Tukey, P.A.: Graphical Methods for Data Analysis, p. 62. Wadsworth and Brooks/Cole (1983)

    Google Scholar 

  14. Fierrez-Aguilar, J., Muñoz-Serrano, L., Alonso-Fernandez, F., Ortega-Garcia, J.: On the effects of image quality degradation on minutiae and ridge-based automatic fingerprint recognition. In IEEE International Carnahan Conference on Security Technology (October 2005)

    Google Scholar 

  15. Martin, A., Doddington, G.R., Kamm, T., Ordowski, M., Przybocki, M.A.: The DET curve in assessment of detection task performance. In: Proceedings of Eurospeech, pp. 1895–1898. Rhodes, Italy, Greece (1997)

    Google Scholar 

  16. Mansfield, A.J., Wayman, J.L.: Best practices in testing and reporting performance of biometric devices. National Physics Laboratory Report CMSC 14/02, August 2002, http://www.cesg.gov.uk/site/ast/biometrics/media/BestPractice.pdf (2002)

  17. Simon-Zorita, D., Ortega-Garcia, J., Fierrez-Aguilar, J., Gonzalez-Rodriguez, J.: Image quality and position variability assessment in minutiae-based fingerprint verification. IEE Proceedings on Vision, Image and Signal Processing, vol. 150, no. 6, pp. 395–401, December 2003, special Issue on Biometrics on the Internet (2003)

    Google Scholar 

  18. Yoshida, A., Hara, M.: Fingerprint image quality metrics that guarantees matching accuracy. In: Procedings of NIST Biometric Quality Workshop. NEC Corp., March 2006, http://www.itl.nist.gov/iad/894.03/quality/workshop/presentations.html

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Tabassi, E., Grother, P. (2009). Biometric Sample Quality. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_119

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