Comparison of ART-2 and SOFM based neural network verifiers
The Carpenter-Grosberg ART-2 and Kohonen Self-organizing Feature Map (SOFM) have been developed for the clustering of input vectors and have been commonly used as unsupervised learned clasifiers. In this paper we describe the use of these neuralnetwork models for signature verification. The architecture of the verifiers and achieved results are discussed here and ideas for future research are also suggested.