Towards human-assisted signature recognition: Improving biometric systems through attribute-based recognition

This work explores human-assisted schemes for improving automatic signature recognition systems. We present a crowdsourcing experiment to establish the human baseline performance for signature recognition tasks and a novel attribute-based semi-automatic signature verification system inspired in FDE analysis. We present different experiments over a public database and a self-developed tool for the manual annotation of signature attributes. The results demonstrate the benefits of attribute-based recognition approaches and encourage to further research in the capabilities of human intervention to improve the performance of automatic signature recognition systems.

[1]  Miguel Angel Ferrer-Ballester,et al.  On-line signature recognition through the combination of real dynamic data and synthetically generated static data , 2015, Pattern Recognit..

[2]  Diana Harrison,et al.  Handwriting Examination: Meeting the Challenges of Science and the Law , 2009 .

[3]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Julian Fiérrez,et al.  Soft Biometrics and Their Application in Person Recognition at a Distance , 2014, IEEE Transactions on Information Forensics and Security.

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

[6]  Cinthia Obladen de Almendra Freitas,et al.  The graphology applied to signature verification , 2005 .

[7]  Berrin A. Yanikoglu,et al.  Identity authentication using improved online signature verification method , 2005, Pattern Recognit. Lett..

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

[9]  Anil K. Jain,et al.  Suspect identification based on descriptive facial attributes , 2014, IEEE International Joint Conference on Biometrics.

[10]  Giuseppe Pirlo,et al.  Automatic Signature Verification: The State of the Art , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Shree K. Nayar,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence Describable Visual Attributes for Face Verification and Image Search , 2022 .

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

[13]  Mark S. Nixon,et al.  Soft Biometrics; Human Identification Using Comparative Descriptions , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ben M. Herbst,et al.  Off-line signature verification: A comparison between human and machine performance , 2006 .

[15]  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.

[16]  Rama Chellappa,et al.  Attribute-based continuous user authentication on mobile devices , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

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

[18]  Robert Sabourin,et al.  A Human-Centric Off-Line Signature Verification System , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[19]  Marcus Liwicki,et al.  Part-Based Automatic System in Comparison to Human Experts for Forensic Signature Verification , 2013, 2013 12th International Conference on Document Analysis and Recognition.