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

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


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.


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.



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


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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