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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/323

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dc.contributor.authorBajaj, Reena-
dc.contributor.authorChaudhury, Santanu-
dc.identifier.citationPattern Recognition, 30(1), 1-7en
dc.description.abstractThis paper is concerned with signature verification. Three different types of global features have been used for the classification of signatures. Feed-forward neural net based classifiers have been used. The features used for the classification are projection moments and upper and lower envelope based characteristics. Output of the three classifiers is combined using a connectionist scheme. Combination of these feature based classifiers for signature verification is the unique feature of this work. Experimental results show that combination of the classifiers increases reliability of the recognition results.en
dc.format.extent243965 bytes-
dc.subjectSignature verificationen
dc.subjectProjection momentsen
dc.subjectNeural neten
dc.subjectCombination of classifiersen
dc.titleSignature verification using multiple neural classifiersen
Appears in Collections:Electrical Engineering

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