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Dynamic Signatures as Forensic Evidence: A New Expert Tool Including Population Statistics

  • Ruben Vera-Rodriguez
  • Julian Fierrez
  • Javier Ortega-Garcia
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

This chapter presents a new tool specifically designed to carry out dynamic signature forensic analysis and give scientific support to forensic handwriting examiners (FHEs). Traditionally FHEs have performed forensic analysis of paper-based signatures for court cases, but with the rapid evolution of the technology, nowadays they are being asked to carry out analysis based on signatures acquired by digitizing tablets more and more often. In some cases, an option followed has been to obtain a paper impression of these signatures and carry out a traditional analysis, but there are many deficiencies in this approach regarding the low spatial resolution of some devices compared to original offline signatures and also the fact that the dynamic information, which has been proved to be very discriminative by the biometric community, is lost and not taken into account at all. The tool we present in this chapter allows the FHEs to carry out a forensic analysis taking into account both the traditional offline information normally used in paper-based signature analysis, and also the dynamic information of the signatures. Additionally, the tool incorporates two important functionalities, the first is the provision of statistical support to the analysis by including population statistics for genuine and forged signatures for some selected features, and the second is the incorporation of an automatic dynamic signature matcher, from which a likelihood ratio (LR) can be obtained from the matching comparison between the known and questioned signatures under analysis. An example case is also reported showing how the tool can be used to carry out a forensic analysis of dynamic signatures.

Keywords

Dynamic Time Warping Dynamic Information Dynamic Signature Forensic Analysis Signature Verification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work has been supported by project CogniMetrics TEC2015-70627-R (MINECO/FEDER) and in part by Cecabank e-BioFirma2 Contract.

References

  1. 1.
    Huber RA, Headrick AM (1999) Handwriting identification: facts and fundamentals. CRC PressGoogle Scholar
  2. 2.
    Found B, Dick D, Rogers D (1994) The structure of forensic handwriting and signature comparisons. Int J Speech Lang Law Forensic Linguist 1:183–196CrossRefGoogle Scholar
  3. 3.
    Found B, Rogers D (1999) Documentation of forensic handwriting comparison and identification method: a modular approach. J Forensic Doc Examination 12:1–68Google Scholar
  4. 4.
    de la Uz Jimenez J (2013) Manual de Grafistica. Tirant Lo BlanchGoogle Scholar
  5. 5.
    Vinals F (2008) Boletin Electronico num. 8, tech. rep., Instituto de Ciencias del GrafismoGoogle Scholar
  6. 6.
    Galende-Diaz JC, Gomez-Barajas C (2008) En busca de la falsedad documental: La figura del perito caligrafico. In: Proceedings of the VII Jornadas Cientificas Sobre Documentacion Contemporanea (1868–2008), pp 193–231, Univ. Complutense de MadridGoogle Scholar
  7. 7.
    Alewijnse L (2013) Forensic signature examination. In: Tutorial at international workshop on automated forensic handwriting analysis (AFHA)Google Scholar
  8. 8.
    Harralson HH (2012) Forensic document examination of electronically captured signatures. Digit Evid Elec Signat L Rev 9:67–73Google Scholar
  9. 9.
    Ahmad SMS, Ling LY, Anwar RM, Faudzi MA, Shakil A (2013) Analysis of the effects and relationship of perceived handwritten signature’s size, graphical complexity, and legibility with dynamic parameters for forged and genuine samples. J Forensic Sci 58(3):724–731CrossRefGoogle Scholar
  10. 10.
    Mohammed MCLA, Found B, Rogers D (2011) The dynamic character of disguise behavior for text-based, mixed, and stylized signatures. J Forensic Sci 56Google Scholar
  11. 11.
    Franke K (2009) Analysis of authentic signatures and forgeries. In: Geradts Z, Franke K, Veenman C (eds) Computational forensics. Lecture notes in computer science, vol 5718. Springer, Berlin, pp 150–164Google Scholar
  12. 12.
    Fierrez J, Ortega-Garcia J (2008) Handbook of biometrics, ch. On-line signature verification. Springer, pp 189–209Google Scholar
  13. 13.
    Fierrez J, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) HMM-based on-line signature verification: feature extraction and signature modeling. Pattern Recognit Lett 28:2325–2334CrossRefGoogle Scholar
  14. 14.
    Martinez-Diaz M, Fierrez J, Krish RP, Galbally J (2014) Mobile signature verification: feature robustness and performance comparison. IET Biometrics 3:267–277CrossRefGoogle Scholar
  15. 15.
    Houmani N, Mayoue A, Garcia-Salicetti S, Dorizzi B, Khalil M, Moustafa M, Abbas H, Muramatsu D, Yanikoglu B, Kholmatov A, Martinez-Diaz M, Fierrez J, Ortega-Garcia J, Alcob JR, Fabregas J, Faundez-Zanuy M, Pascual-Gaspar J, Cardeoso-Payo V, Vivaracho-Pascual C (2012) Biosecure signature evaluation campaign (BSEC2009): evaluating online signature algorithms depending on the quality of signatures. Pattern Recognit 45:993–1003CrossRefGoogle Scholar
  16. 16.
    Martinez-Diaz M, Fierrez J, Hangai S (2009) Encyclopedia of biometrics, ch.  Signature matching. SpringerGoogle Scholar
  17. 17.
    Malik M, Liwicki M, Dengel A, Found B (2013) Man vs. machine: a comparative analysis for forensic signature verification. In: Proceedings of the biennial conference of the international graphonomics societyGoogle Scholar
  18. 18.
    Vera-Rodriguez R, Fierrez J, Ortega-Garcia J, Acien A, Tolosana R (2015) e-BioSign tool: towards scientific assessment of dynamic signatures under forensic conditions. In: Proceedings of the IEEE international conference on biometrics: theory, applications and systems (BTAS), (Washington), Sept 2015Google Scholar
  19. 19.
    Richiardi J, Ketabdar H, Drygajlo A (2005) Local and global feature selection for on-line signature verification. In: Proceedings of the eighth international conference on document analysis and recognition, vol 2, pp 625–629, Aug 2005Google Scholar
  20. 20.
    Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recognit 38(12):2270–2285CrossRefGoogle Scholar
  21. 21.
    Vera-Rodriguez R, Tolosana R, Ortega-Garcia J, Fierrez J (2015) e-BioSign: stylus- and finger-input multi-device database for dynamic signature recognition. In: Proceedings of the 3rd international workshop on biometrics and forensics (IWBF), (Norway). IEEE Press, March 2015Google Scholar
  22. 22.
    Tolosana R, Vera-Rodriguez R, Ortega-Garcia J, Fierrez J (2015) Preprocessing and feature selection for improved sensor interoperability in online biometric signature verification. IEEE Access 3:478–489CrossRefGoogle Scholar
  23. 23.
    Gonzalez-Rodriguez J, Fierrez-Aguilar J, Ramos-Castro D, Ortega-Garcia J (2005) Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems. Forensic Sci Int 155:126–140CrossRefGoogle Scholar
  24. 24.
    Morrison GS (2011) Measuring the validity and reliability of forensic likelihood-ratio systems. Sci Justice 51(3):91–98CrossRefGoogle Scholar
  25. 25.
    Ramos D, Gonzalez-Rodriguez J, Zadora G, Aitken C (2013) Information-theoretical assessment of the performance of likelihood ratio computation methods. J Forensic Sci 58(6):1503–1518Google Scholar
  26. 26.
    Gonzalez-Rodriguez J, Rose P, Ramos D, Toledano DT, Ortega-Garcia J (2007) Emulating dna: rigorous quantification of evidential weight in transparent and testable forensic speaker recognition. IEEE Trans Audio Speech Lang Process 15:2104–2115CrossRefGoogle Scholar
  27. 27.
    Brummer N, de Villiers E (2011) The BOSARIS toolkit user guide: theory, algorithms and code for binary classifier score processing. Tech. Rep, AgnitioGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ruben Vera-Rodriguez
    • 1
  • Julian Fierrez
    • 1
  • Javier Ortega-Garcia
    • 1
  1. 1.ATVS - Biometric Recognition Group, Escuela Politecnica SuperiorUniversidad Autonoma de MadridMadridSpain

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