Online signature verification using neural network and pearson correlation features

Vahab Iranmanesh, Sharifah Mumtazah Syed Ahmad, Wan Azizun Wan Adnan, Fahad Layth Malallah, Salman Yussof

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In this paper, we proposed a method for feature extraction in online signature verification. We first used signature coordinate points and pen pressure of all signatures, which are available in the SIGMA database. Then, Pearson correlation coefficients were selected for feature extraction. The obtained features were used in back-propagation neural network for verification. The results indicate an accuracy of 82.42%.

Original languageEnglish
Title of host publication2013 IEEE Conference on Open Systems, ICOS 2013
PublisherIEEE Computer Society
Pages18-21
Number of pages4
ISBN (Print)9781479902859
DOIs
Publication statusPublished - 01 Jan 2013
Event2013 IEEE Conference on Open Systems, ICOS 2013 - Kuching, Sarawak, Malaysia
Duration: 02 Dec 201304 Dec 2013

Publication series

Name2013 IEEE Conference on Open Systems, ICOS 2013

Other

Other2013 IEEE Conference on Open Systems, ICOS 2013
CountryMalaysia
CityKuching, Sarawak
Period02/12/1304/12/13

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Iranmanesh, V., Ahmad, S. M. S., Wan Adnan, W. A., Malallah, F. L., & Yussof, S. (2013). Online signature verification using neural network and pearson correlation features. In 2013 IEEE Conference on Open Systems, ICOS 2013 (pp. 18-21). [6735040] (2013 IEEE Conference on Open Systems, ICOS 2013). IEEE Computer Society. https://doi.org/10.1109/ICOS.2013.6735040