Citation

Ooi, Shih Yin and Teoh, Andrew Beng Jin and Pang, Ying Han and Hiew, Bee Yan (2016) Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network. Applied Soft Computing, 40. pp. 274-282. ISSN 1568-4946

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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.

Item Type:	Article
Uncontrolled Keywords:	Offline signature verification; Discrete Radon transform; Principal component analysis; Probabilistic neural network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Nov 2017 11:06
Last Modified:	28 Nov 2017 11:06
URII:	http://shdl.mmu.edu.my/id/eprint/6515

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