Please use this identifier to cite or link to this item: http://dspace2020.uniten.edu.my:8080/handle/123456789/9057
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dc.contributor.authorIranmanesh, V.
dc.contributor.authorAhmad, S.M.S.
dc.contributor.authorWan Adnan, W.A.
dc.contributor.authorMalallah, F.L.
dc.contributor.authorYussof, S.
dc.date.accessioned2018-02-21T04:53:17Z-
dc.date.available2018-02-21T04:53:17Z-
dc.date.issued2013
dc.identifier.urihttp://dspace.uniten.edu.my/jspui/handle/123456789/9057-
dc.description.abstractIn 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%. © 2013 IEEE.
dc.titleOnline signature verification using neural network and pearson correlation features
item.fulltextNo Fulltext-
item.grantfulltextnone-
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