Performance of an Off-Line Signature Verification Method Based on Texture Features on a Large Indic-Script Signature Dataset

Pal, S; Alaei, A; Pal, U; Blumenstein, M

Performance of an Off-Line Signature Verification Method Based on Texture Features on a Large Indic-Script Signature Dataset

Pal, S Alaei, A Pal, U Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, 2016, pp. 72 - 77
Issue Date:: 2016-06-10

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Field	Value	Language
dc.contributor.author	Pal, S	en_US
dc.contributor.author	Alaei, A	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2016-06-10	en_US
dc.identifier.citation	Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, 2016, pp. 72 - 77	en_US
dc.identifier.isbn	9781509017928	en_US
dc.identifier.uri	http://hdl.handle.net/10453/92541
dc.description.abstract	© 2016 IEEE. In this paper, a signature verification method based on texture features involving off-line signatures written in two different Indian scripts is proposed. Both Local Binary Patterns (LBP) and Uniform Local Binary Patterns (ULBP), as powerful texture feature extraction techniques, are used for characterizing off-line signatures. The Nearest Neighbour (NN) technique is considered as the similarity metric for signature verification in the proposed method. To evaluate the proposed verification approach, a large Bangla and Hindi off-line signature dataset (BHSig260) comprising 6240 (260×24) genuine signatures and 7800 (260×30) skilled forgeries was introduced and further used for experimentation. We further used the GPDS-100 signature dataset for a comparison. The experiments were conducted, and the verification accuracies were separately computed for the LBP and ULBP texture features. There were no remarkable changes in the results obtained applying the LBP and ULBP features for verification when the BHSig260 and GPDS-100 signature datasets were used for experimentation.	en_US
dc.relation.ispartof	Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016	en_US
dc.relation.isbasedon	10.1109/DAS.2016.48	en_US
dc.title	Performance of an Off-Line Signature Verification Method Based on Texture Features on a Large Indic-Script Signature Dataset	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:

© 2016 IEEE. In this paper, a signature verification method based on texture features involving off-line signatures written in two different Indian scripts is proposed. Both Local Binary Patterns (LBP) and Uniform Local Binary Patterns (ULBP), as powerful texture feature extraction techniques, are used for characterizing off-line signatures. The Nearest Neighbour (NN) technique is considered as the similarity metric for signature verification in the proposed method. To evaluate the proposed verification approach, a large Bangla and Hindi off-line signature dataset (BHSig260) comprising 6240 (260×24) genuine signatures and 7800 (260×30) skilled forgeries was introduced and further used for experimentation. We further used the GPDS-100 signature dataset for a comparison. The experiments were conducted, and the verification accuracies were separately computed for the LBP and ULBP texture features. There were no remarkable changes in the results obtained applying the LBP and ULBP features for verification when the BHSig260 and GPDS-100 signature datasets were used for experimentation.

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http://hdl.handle.net/10453/92541