On the Use of Score Ratio with Distance-Based Classifiers in Biometric Signature Recognition

  • Carlos Vivaracho-PascualEmail author
  • Arancha Simon-Hurtado
  • Esperanza Manso-Martinez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)


Biometric user verification or authentication is a pattern recognition problem that can be stated as a basic hypothesis test: X is from client C (\(H_0\)) vs. X is not from client C (\(H_1\)), where X is the biometric input sample (face, fingerprint, etc.). When probabilistic classifiers are used (e.g., Hidden Markov Models), the decision is typically performed by means of the likelihood ratio: \({P(X/H_0)}/{P(X/H_1)}\). However, as far as we know, this ratio is not usually performed when distance-based classifiers (e.g., Dynamic Time Warping) are used. Following that idea, we propose, here, to perform the decision based not only on the score (“score” being the classifier output) supposing X is from the client (\(H_0\)), but also using the score supposing X is not from the client (\(H_1\)), by means of the ratio between both scores: the score ratio. A first approach to this proposal can be seen in this work, showing that to use the score ratio can be an interesting technique to improve distance-based biometric systems. This research has focused on the biometric signature, where several state of the art systems based on distance can be found. Here, the score ratio proposal is tested in three of them, achieving great improvements in the majority of the tests performed. The best verification results have been achieved with the use of the score ratio, improving the best ones without the score ratio by, on average, 24 %.


Score ratio Signature verification Distance-based classifier 



Thanks to A. F. Hynds B.A. Dip. TEFL for revising the English grammar.


  1. 1.
    Rua, E.A., Castro, J.L.A.: Online signature verification based on generative models. IEEE Trans. Syst. Man Cybern. B Cybern. 42(4), 1231–1242 (2012)CrossRefGoogle Scholar
  2. 2.
    Furui, S.: An overview of the speaker recognition technology. In: Proceedings of the Workshop on Automatic Speaker Recognition Identification and Verification, Martigny, Switzerland, pp. 1–9, 5–7 Apr 1994Google Scholar
  3. 3.
    Houmani, N., et al.: Biosecure signature evaluation campaign (BSEC’2009): evaluating online signature algorithms depending on the quality of signatures. Pattern Recogn. 45(3), 993–1003 (2012)CrossRefGoogle Scholar
  4. 4.
    Nanni, L., Lumini, A.: A supervised method to discriminate between impostors and genuine in biometry. Expert Syst. Appl. 36(7), 10401–10407 (2009)CrossRefGoogle Scholar
  5. 5.
    Ortega-Garcia, J., Fierrez, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Hernaez, I., Espinosa, V., Satue, A., Igarza, J.J., Vivaracho, C., Escudero, D., Moro, Q.I.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vis. Image Signal Process. 150(6), 395–401 (2003)CrossRefGoogle Scholar
  6. 6.
    Pascual-Gaspar, J.M., Faundez-Zanuy, M., Vivaracho, C.: Fast on-line signature recognition based on VQ with time modeling. Eng. Appl. Artif. Intell. 24(2), 368–377 (2011)CrossRefGoogle Scholar
  7. 7.
    Vivaracho-Pascual, C., Simon-Hurtado, A., Manso-Martinez, E., Pascual-Gaspar, J.M.: A new proposal for score normalization in biometric signature recognition based on client threshold prediction. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 1128–1133, December 2012Google Scholar
  8. 8.
    Vivaracho-Pascual, C., Faundez-Zanuy, M., Pascual, J.M.: An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances. Pattern Recogn. 42(1), 183–193 (2009)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carlos Vivaracho-Pascual
    • 1
    Email author
  • Arancha Simon-Hurtado
    • 1
  • Esperanza Manso-Martinez
    • 1
  1. 1.Department InformáticaUniversidad de ValladolidValladolidSpain

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