Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Fusion, Quality-Based

  • Norman Poh
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_162

Synonym

Definition

Quality-based fusion refers to the use of quality measures in combining several biometric system outputs. Quality measures are an array of measurements quantifying the degree of excellence or conformance of biometric samples to some predefined criteria known to influence the system performance. Examples of quality measures for face biometrics are focus, contrast, and face detection reliability; and for iris biometrics are iris texture richness, the area of iris used for matching, and iris detection reliability. In quality-based fusion, the match scores of biometric samples of higher quality are considered more important, i.e., given higher weights, in order to compute the final combined score.

Introduction

Quality-based fusion in the context of multibiometric systems is more challenging than multi-algorithmic systems because quality measures of the different biometric modalities are not comparable. This implies that quality-based fusion...

This is a preview of subscription content, log in to check access

References

  1. 1.
    Nandakumar, K., Chen, Y., Dass, S., Jain, A.: Quality-based score level fusion in multibiometric systems. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR), pp. 473–476. Hong Kong (2006)Google Scholar
  2. 2.
    Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J., Bigun, J.: Kernel-based multimodal biometric verification using quality signals. In: Defense and Security Symposium, Workshop on Biometric Technology for Human Identification, Proceedings of SPIE, vol. 5404, pp. 544–554 (2004)Google Scholar
  3. 3.
    Bigun, J., Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Multimodal biometric authentication using quality signals in mobile communications. In: 12th International Conference on Image Analysis and Processing, pp. 2–13. Mantova (2003)Google Scholar
  4. 4.
    Kryszczuk, K., Richiardi, J., Prodanov, P., Drygajlo, A.: Error handling in multimodal biometric systems using reliability measures. In: Proceedings of the 12th European Conference on Signal Processing. Antalya, Turkey (2005)Google Scholar
  5. 5.
    Kittler, J., Poh, N., Fatukasi, O., Messer, K., Kryszczuk, K., Richiardi, J., Drygajlo, A.: Quality dependent fusion of intramodal and multimodal biometric experts. In: Proceedings of SPIE Defense and Security Symposium, Workshop on Biometric Technology for Human Identification, vol. 6539 (2007)Google Scholar
  6. 6.
    Poh, N., Heusch, G., Kittler, J.: On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers. In: LNCS 4472, Multiple Classifiers System (MCS), pp. 344–356. Prague (2007)Google Scholar
  7. 7.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press (1999)Google Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer (2001)Google Scholar
  9. 9.
    Vapnik, V.N.: Statistical Learning Theory. Springer (1998)Google Scholar
  10. 10.
    Jain, A., Nandakumar, K., Ross, A.: Score normalisation in multimodal biometric systems. Pattern Recognit. 38(12),2270–2285 (2005)CrossRefGoogle Scholar
  11. 11.
    Toh, K.A., Yau, W.Y., Lim, E., Chen, L., Ng., C.H.: Fusion of Auxiliary Information for Multimodal Biometric Authentication. In: LNCS 3072, International Conference on Biometric Authentication (ICBA), pp. 678–685. Hong Kong (2004)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Norman Poh
    • 1
  1. 1.CVSSP, FEPSUniversity of Surrey GuildfordSurreyUK