e-BioSign tool: Towards scientific assessment of dynamic signatures under forensic conditions

This paper 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 off-line 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 paper allows the FHEs to carry out a forensic analysis taking into account both the traditional off-line 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.

[1]  Anil K. Jain,et al.  Encyclopedia of Biometrics , 2015, Springer US.

[2]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[3]  Valentín Cardeñoso-Payo,et al.  BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures , 2012, Pattern Recognit..

[4]  Bryan Found,et al.  The structure of forensic handwriting and signature comparisons , 2013 .

[5]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[6]  Anil K. Jain,et al.  On-line signature verification, , 2002, Pattern Recognit..

[7]  Julian Fiérrez,et al.  E-biosign: stylus- and finger-input multi-device database for dynamic signature recognition , 2015, 3rd International Workshop on Biometrics and Forensics (IWBF 2015).

[8]  Javier Ortega-Garcia,et al.  Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems. , 2005, Forensic science international.

[9]  Doroteo Torre Toledano,et al.  Emulating DNA: Rigorous Quantification of Evidential Weight in Transparent and Testable Forensic Speaker Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Katrin Franke Analysis of Authentic Signatures and Forgeries , 2009, IWCF.

[11]  Grzegorz Zadora,et al.  Information‐Theoretical Assessment of the Performance of Likelihood Ratio Computation Methods , 2013, Journal of forensic sciences.

[12]  Roy Huber,et al.  Handwriting Identification: Facts and Fundamentals , 1999 .

[13]  Geoffrey Stewart Morrison,et al.  Measuring the validity and reliability of forensic likelihood-ratio systems. , 2011, Science & justice : journal of the Forensic Science Society.

[14]  Bryan Found,et al.  The Dynamic Character of Disguise Behavior for Text‐based, Mixed, and Stylized Signatures , 2011, Journal of forensic sciences.

[15]  Heidi H. Harralson Forensic document examination of electronically captured signatures , 2014 .

[16]  Julian Fiérrez,et al.  HMM-based on-line signature verification: Feature extraction and signature modeling , 2007, Pattern Recognit. Lett..

[17]  Julian Fiérrez,et al.  Preprocessing and Feature Selection for Improved Sensor Interoperability in Online Biometric Signature Verification , 2015, IEEE Access.

[18]  Seiichiro Hangai,et al.  Signature Matching , 2009, Encyclopedia of Biometrics.

[19]  Jonas Richiardi,et al.  Local and global feature selection for on-line signature verification , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[20]  Sharifah Mumtazah Syed Ahmad,et al.  Analysis of the Effects and Relationship of Perceived Handwritten Signature's Size, Graphical Complexity, and Legibility with Dynamic Parameters for Forged and Genuine Samples , 2013, Journal of forensic sciences.

[21]  Julian Fiérrez,et al.  Mobile signature verification: feature robustness and performance comparison , 2014, IET Biom..