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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 classifiers. In this paper we describe the use of these neural network models for signature verification. The biometric data of all signatures were acquired by a special digital data acquisition pen and fast wavelet transformation was used for feature extraction. The part of genuine signature data was used for training both signature verifiers. The architecture of the verifiers and achieved results are discussed here and ideas for future research are also suggested.