Off-line signature verification using G-SURF

Pal, S; Chanda, S; Pal, U; Franke, K; Blumenstein, M

Off-line signature verification using G-SURF

Pal, S Chanda, S Pal, U Franke, K Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: International Conference on Intelligent Systems Design and Applications, ISDA, 2012, pp. 586 - 591
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Pal, S	en_US
dc.contributor.author	Chanda, S	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Franke, K	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	International Conference on Intelligent Systems Design and Applications, ISDA, 2012, pp. 586 - 591	en_US
dc.identifier.isbn	9781467351188	en_US
dc.identifier.issn	2164-7143	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121218
dc.description.abstract	In the field of biometric authentication, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an easy method for authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Signatures provide a secure means for confirmation and authorization in legal documents. So nowadays, signature identification and verification becomes an essential component in automating the rapid processing of documents containing embedded signatures. Sometimes, part-based signature verification can be useful when a questioned signature has lost its original shape due to inferior scanning quality. In order to address the above-mentioned adverse scenario, we propose a new feature encoding technique. This feature encoding is based on the amalgamation of Gabor filter-based features with SURF features (G-SURF). Features generated from a signature are applied to a Support Vector Machine (SVM) classifier. For experimentation, 1500 (50x30) forgeries and 1200 (50x24) genuine signatures from the GPDS signature database were used. A verification accuracy of 97.05% was obtained from the experiments. © 2012 IEEE.	en_US
dc.relation.ispartof	International Conference on Intelligent Systems Design and Applications, ISDA	en_US
dc.relation.isbasedon	10.1109/ISDA.2012.6416603	en_US
dc.title	Off-line signature verification using G-SURF	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In the field of biometric authentication, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an easy method for authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Signatures provide a secure means for confirmation and authorization in legal documents. So nowadays, signature identification and verification becomes an essential component in automating the rapid processing of documents containing embedded signatures. Sometimes, part-based signature verification can be useful when a questioned signature has lost its original shape due to inferior scanning quality. In order to address the above-mentioned adverse scenario, we propose a new feature encoding technique. This feature encoding is based on the amalgamation of Gabor filter-based features with SURF features (G-SURF). Features generated from a signature are applied to a Support Vector Machine (SVM) classifier. For experimentation, 1500 (50x30) forgeries and 1200 (50x24) genuine signatures from the GPDS signature database were used. A verification accuracy of 97.05% was obtained from the experiments. © 2012 IEEE.

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http://hdl.handle.net/10453/121218