Robustness of Score Normalization in Multibiometric Systems

  • Radhey ShyamEmail author
  • Yogendra Narain Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9478)


This paper presents an evaluation of normalization techniques of matching scores on the recognition performance of a multibiometric system. We present two score normalization techniques, namely modified-linear-tanh-linear (MLTL) and four-segments-double-sigmoid (FSDS) that are found to be robust in achieving the recognition performance to the optimum value. The techniques are tested in fusion of the two face recognition methods Fisherface and A-LBP on the dataset of uncontrolled environments. In particular, AT & T (ORL) face dataset is used in this experiment. The performance of the MLTL and FSDS score normalization techniques are compared with the existing normalization techniques, for instance min-max, tanh and linear-tanh-linear (LTL). The proposed normalization techniques show the significant improvement in the recognition performance of the multibiometric system over the known techniques.


Face recognition Multibiometric Normalization Identification 



The authors acknowledge the Institute of Engineering and Technology (IET), Lucknow, Uttar Pradesh Technical University (UPTU), Lucknow for their partial financial support to carry out this research under the Technical Education Quality Improvement Programme (TEQIP-II) grant.


  1. 1.
    Jain, A.K., Ross, A.: Multibiometric systems. Commun. ACM 47(1), 34–40 (2004)CrossRefGoogle Scholar
  2. 2.
    Snelick, R., Uludag, U., Mink, A., Indovina, M., Jain, A.: Large scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 450–455 (2005)CrossRefGoogle Scholar
  3. 3.
    Bolle, R.M., Ratha, N.K., Pankanti, S.: Error analysis of pattern recognition systems the subsets bootstrap. Comput. Vis. Image Underst. 93(1), 1–33 (2004)CrossRefGoogle Scholar
  4. 4.
    Shyam, R., Singh, Y.N.: Identifying individuals using multimodal face recognition techniques. Procedia Comput. Sci. Elsevier 48, 666–672 (2015)CrossRefGoogle Scholar
  5. 5.
    Singh, Y.N., Singh, S.K., Gupta, P.: Fusion of electrocardiogram with unobtrusive biometrics: an efficient individual authentication system. Pattern Recogn. Lett. Elsevier 33(11), 1932–1941 (2012)CrossRefGoogle Scholar
  6. 6.
    Singh, Y.N.: Human recognition using fisher’s discriminant analysis of heartbeat interval features and ECG morphology. Neurocomputing Elsevier 167(2015), 322–335 (2015)CrossRefGoogle Scholar
  7. 7.
    Singh, Y.N., Gupta, P.: Quantitative evaluation of normalization techniques of matching scores in multimodal biometric systems. In: Lee, Seong-Whan, Li, Stan Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 574–583. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  8. 8.
    Shyam, R., Singh, Y.N.: A taxonomy of 2D and 3D face recognition methods. In: Proceedings of 1st International Conference on Signal Processing and Integrated Networks (SPIN 2014), pp. 749–754. IEEE, February 2014Google Scholar
  9. 9.
    Shyam, R., Singh, Y.N.: Evaluation of eigenfaces and fisherfaces using bray curtis dissimilarity metric. In: Proceedings of 9th IEEE International Conference on Industrial and Information Systems (ICIIS 2014), pp. 1–6. IEEE, Gwalior, December 2014Google Scholar
  10. 10.
    Shyam, R., Singh, Y.N.: Face recognition using augmented local binary patterns and bray curtis dissimilarity metric. In: Proceedings of 2nd International Conference on Signal Processing and Integrated Networks (SPIN 2015), pp. 779–784. IEEE, Noida, February 2015Google Scholar
  11. 11.
    Shyam, R., Singh, Y.N.: Analysis of local descriptors for human face recognition. In: Smart Innovation, Systems and Technologies, vol. 43, pp. 263–269. Springer, October 2015Google Scholar
  12. 12.
    Shyam, R., Singh, Y.N.: Automatic face recognition in digital world. Adv. Comput. Sci. Inf. Technol. (ACSIT) 2(1), 64–70 (2015)Google Scholar
  13. 13.
    Shyam, R., Singh, Y.N.: Recognizing individuals from unconstrained facial images. Adv. Intell. Syst. Comput. Ser. Springer 384, 383–392 (2015)Google Scholar
  14. 14.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  15. 15.
    Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recogn. 38(12), 2270–2285 (2005)CrossRefGoogle Scholar
  16. 16.
    Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, FL, December 1994Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringInstitute of Engineering and TechnologyLucknowIndia

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