Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features

One of the biggest challenges in on-line signature verification is the detection of skilled forgeries. In this paper, we propose a novel scheme, based on the Kinematic Theory of rapid human movements and its associated Sigma LogNormal model, to improve the performance of on-line signature verification systems. The approach combines the high performance of DTW-based systems in verification tasks, with the high potential for skilled forgery detection of the Kinematic Theory of rapid human movements. Experiments were carried out on the publicly available BiosecurID multimodal database, comprising 400 subjects. Results show that the performance of the DTW-based system improves for both skilled and random forgeries.

[1]  Brian C. Lovell,et al.  An Automatic Off-Line Signature Verification and Forgery Detection System , 2008 .

[2]  Azriel Rosenfeld,et al.  Off-line skilled forgery detection using stroke and sub-stroke properties , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Javier Ortega-Garcia,et al.  Fusion of static image and dynamic information for signature verification , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[4]  Wataru Ohyama,et al.  ICDAR 2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp 2013) , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[5]  Marcus Liwicki,et al.  Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011) , 2011, 2011 International Conference on Document Analysis and Recognition.

[6]  Réjean Plamondon,et al.  Development of a Sigma-Lognormal representation for on-line signatures , 2009, Pattern Recognit..

[7]  Javier Garrido Salas,et al.  BiosecurID: a multimodal biometric database , 2009, Pattern Analysis and Applications.

[8]  Sébastien Marcel,et al.  Anti-spoofing in Action: Joint Operation with a Verification System , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

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

[10]  Marcus Liwicki,et al.  Forensic Signature Verification Competition 4NSigComp2010 - Detection of Simulated and Disguised Signatures , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[11]  Julian Fiérrez,et al.  Robustness of Signature Verification Systems to Imitators with Increasing Skills , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[12]  Azriel Rosenfeld,et al.  Forgery Detection by Local Correspondence , 2001, Int. J. Pattern Recognit. Artif. Intell..

[13]  V. L. Blankers,et al.  ICDAR 2009 Signature Verification Competition , 2009, ICDAR.

[14]  Somaya Al-Máadeed,et al.  An Online Signature Verification System for Forgery and Disguise Detection , 2012, ICONIP.

[15]  M. Yusof,et al.  Signature verification and forgery detection system , 2003, Proceedings. Student Conference on Research and Development, 2003. SCORED 2003..

[16]  Arun Ross,et al.  Combining match scores with liveness values in a fingerprint verification system , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

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