Signature verification using multiple neural classifiers

This 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. Copyright (C) 1996 Pattern Recognition Society.