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Confidence voting method ensemble applied to off-line signature verification

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Abstract

In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalization by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3 % in the case of skilled forgeries and 2.31 % in the case of random forgeries.

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References

  1. Abreu M, Fairhurst M (2010) Improving forgery detection in off-line forensic signature processing. In: 3rd International conference on crime detection and prevention (ICDP 2009). IET, pp 1–6

  2. Arlandis J, Perez-Cortes J, Cano J (2002) Rejection strategies and confidence measures for a k-nn classifier in an ocr task. In: 16th International conference on pattern recognition ICPR-2002, vol 1. IEEE Computer Society, Québec, pp 576–579

  3. Fierrez-Aguilar J, Alonso-Hermira N, Moreno-Marquez G, Ortega-Garcia J (2004) An off-line signature verification system based on fusion of local and global information. In: Proceeding of European conference on computer vision. Workshop on biometric authentication, BIOAW. LNCS, vol 3087. Springer, Berlin, pp 295–306

  4. Impedovo D, Pirlo G (2008) Automatic signature verification: the state of the art. IEEE Trans Syst Man Cybern Part C: Appl Rev 38(5):609–635

    Article  Google Scholar 

  5. Jain AK, Griess FD, Connell SD (2002) On-line signature verification. Pattern Recognit 35:2963–2972

    Article  MATH  Google Scholar 

  6. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, New York

  7. Maio D, Maltoni D, Cappelli R, Wayman J, Jain A (2002) Fvc 2000: fingerprint verification competition. IEEE Trans Pattern Anal Mach Intell 24(3):402–412. doi:10.1109/34.990140

    Article  Google Scholar 

  8. Martin A, Doddington G, Kamm T, Ordowski M, Przybocki M (1997) The det curve in assessment of detection task performance. In: ESCA European conference on speech communication and technology, EuroSpeech, pp 1895–1898

  9. Oda H, Zhu B, Tokuno J, Onuma M, Kitadai A, Nakagawa M (2006) A compact on-line and off-line combined recognizer. In: Tenth international workshop on frontiers in handwriting recogntion, vol 1, pp 133–138

  10. Ortega-Garcia J, Fierrez-Aguilar J, Simon D, Gonzalez J, MF, Espinosa V, Satue A, Hernaez I, Igarza JJ, Vivaracho C, Escudero D, Moro QI (2003) Mcyt baseline corpus: a bimodal biometric database. IEE Proc Vis Image Signal Process (Special Issue on Biometrics on the Internet) 150(6):395–401

  11. Plamondon R (1994) The design of an on-line signature verification system: from theory to practice. IJPRAI 8(3):795–811

    Google Scholar 

  12. Rico-Juan JR, Iñesta JM (2007) Normalisation of confidence voting methods applied to a fast and written OCR classification. In: Kurzynski M, Puchala E, Wozniak M, Zolnierek A (eds) Computer recognition systems 2. Advances in soft computing, vol 45. Springer, Wroclaw, pp 405–412

  13. Ruiz-Del-Solar J, Devia C, Loncomilla P, Concha F (2008) Offline signature verification using local interest points and descriptors. In: Progress in pattern recognition, image analysis and applications, pp 22–29

  14. Serra J (1982) Image analysis and mathematical morphology. Academic Press, New York

  15. van Breukelen M, Duin RPW, DT, den Hartog J (1998) Handwritten digit recognition by combined classifiers. Comput Linguist 34(4):381–386

  16. van Erp M, Vuurpijl L, Schomaker L (2002) An overview and comparison of voting methods for pattern recognition. In: IWFHR ’02: proceedings of the eighth international workshop on frontiers in handwriting recognition (IWFHR’02). IEEE Computer Society, Washington, p 195

  17. Vellasques E, Oliveira L, Jr AB, Koerich A, Sabourin R (2006) Modeling segmentation cuts using support vector machines. In: Tenth international workshop on frontiers in handwriting recogntion, vol 1, pp 41–46

  18. Yoshiki M, Mitsu Y, Hidetoshi M, Isao Y (2002) An off-line signature verification system using an extracted displacement function. Pattern Recognit Lett 23(13):1569–1577. doi:10.1016/S0167-8655(02)00121–6

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the Spanish CICYT under Spanish MICINN projects TIN2009-14205-CO4-01 and TIN2009-14247-C02-02, and by the Spanish research programme Consolider Ingenio 2010: MIPRCV (CSD2007-00018).

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Correspondence to Juan Ramón Rico-Juan.

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Rico-Juan, J.R., Iñesta, J.M. Confidence voting method ensemble applied to off-line signature verification. Pattern Anal Applic 15, 113–120 (2012). https://doi.org/10.1007/s10044-012-0270-1

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