Cohort Based Approach to Multiexpert Class Verification

  • Josef Kittler
  • Norman Poh
  • Amin Merati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)


We address the problem of cohort based normalisation in multiexpert class verification. We show that there is a relationship between decision templates and cohort based normalisation methods. Thanks to this relationship, some of the recent features of cohort score normalisation techniques can be adopted by decision templates, with the benefit of noise reduction and the ability to compensate for any distribution drift.


Equal Error Rate Class Identity Query Pattern False Acceptance Rate False Rejection Rate 
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  1. 1.
    Aggarwal, G., Ratha, N.K., Bolle, R.M.,Chellappa, R.: Multi-biometric cohort analysis for biometric fusion. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing (2008)Google Scholar
  2. 2.
    Auckenthaler, R., Carey, M., Lloyd-Thomas, H.: Score normalization for text-independant speaker verification systems. Journal of Digital Signal Processing (DSP) 10, 42–54 (2000)CrossRefGoogle Scholar
  3. 3.
    Fukunaga, K., Ando, S.: The optimum non-linear features for a scatter criterion in discriminant analysis. IEEE Tans. Information Theory 23(4), 453–459 (1977)CrossRefzbMATHGoogle Scholar
  4. 4.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Kuncheva, L., Bezdek, J.C., Duin, R.P.W.: Decision Template for Multiple Classifer Fusion: An Experimental Comparison. Pattern Recognition Letters 34, 228–237 (2001)Google Scholar
  6. 6.
    Mariethoz, J., Bengio, S.: A unified framework for score normalization techniques applied to text independent speaker verification. IEEE Signal Processing Letters 12 (2005)Google Scholar
  7. 7.
    Merati, A., Poh, N., Kittler, J.: Extracting discriminative information from cohort models. In: Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS) pp.1–6, (September 2010)Google Scholar
  8. 8.
    Navratil, J., Ramaswamy, G.N.: The awe and mystery of t-norm. In: EUROSPEECH 2003, pp. 2009–2012 (2003)Google Scholar
  9. 9.
    Poh, N., Merati, A., Kitter, J.: Making better biometric decisions with quality and cohort information: A case study in fingerprint verification. In: Proc. 17th European Signal Processing Conf., Eusipco (2009)Google Scholar
  10. 10.
    Poh, N., Bourlai, T., Kittler, J.: A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms. Pattern Recogn. 43(3), 1094–1105 (2010)CrossRefzbMATHGoogle Scholar
  11. 11.
    Tulyakov, S., Zhang, Z., Govindaraju, V.: Comparison of combination methods utilizing t-normalization and second best score model. In: IEEE Conf. on Computer Vision and Pattern Recognition Workshop (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Josef Kittler
    • 1
  • Norman Poh
    • 1
  • Amin Merati
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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