P. Mautner, V. Matoušek, T. Maršálek, and M. Šoule (Czech Republic)
biometrics, signature verification, neural network, SOM, BiSP, fast wavelet transform
The Kohonen Self-organizing Feature Map (SOFM) has been developed for the clustering of input vectors and has been commonly used as unsupervised learned classifiers. In this paper we describe the use of the SOFM neural net work model for signature verification. The biometric data of all signatures were acquired by a special digital data ac quisition pen and fast wavelet transformation was used for feature extraction. Some of the authentic signature data were used for training the SOFM signature verifier. The ar chitecture of the verifier and achieved results are discussed here and ideas for future research are also suggested.
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