Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network

Shih Yin Ooi, Andrew Beng Jin Teoh, Ying Han Pang, Bee Yan Hiew

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Image-based handwritten signature verification is important in most of the financial transactions when a hard copy of signature is needed. Considering the lack of dynamic information from static signature images, we proposed a working framework through hybrid methods of discrete Radon transform (DRT), principal component analysis (PCA) and probabilistic neural network (PNN). The proposed framework aims to distinguish forgeries from genuine signatures based on the image level. Extensive experiments are conducted on our own independent signature database, and a public signature database - MYCT. Equal error rates (EER) of 1.51%, 3.23% and 13.07% are reported, respectively, for random, casual and skilled forgeries of our own database. When working on the MYCT signature database, our proposed approach manages to achieve an EER of 9.87% with 10 training samples.

Original languageEnglish
Pages (from-to)274-282
Number of pages9
JournalApplied Soft Computing Journal
Volume40
DOIs
Publication statusPublished - 2016 Mar 1

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Radon
Principal component analysis
Neural networks
Experiments

All Science Journal Classification (ASJC) codes

  • Software

Cite this

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abstract = "Image-based handwritten signature verification is important in most of the financial transactions when a hard copy of signature is needed. Considering the lack of dynamic information from static signature images, we proposed a working framework through hybrid methods of discrete Radon transform (DRT), principal component analysis (PCA) and probabilistic neural network (PNN). The proposed framework aims to distinguish forgeries from genuine signatures based on the image level. Extensive experiments are conducted on our own independent signature database, and a public signature database - MYCT. Equal error rates (EER) of 1.51{\%}, 3.23{\%} and 13.07{\%} are reported, respectively, for random, casual and skilled forgeries of our own database. When working on the MYCT signature database, our proposed approach manages to achieve an EER of 9.87{\%} with 10 training samples.",
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