Using self-organizing feature map for signature verification

被引：0

作者：

Mautner, P ^{[1
]}

Matousek, V ^{[1
]}

Marsálek, T ^{[1
]}

Soule, M ^{[1
]}

机构：

[1] Univ W Bohemia, Fac Sci Appl, CZ-30614 Plzen, Pilsen, Czech Republic

来源：

Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing | 2004年

关键词：

biometrics; signature verification; neural network; SOM; BiSP; fast wavelet transform;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 network model 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. Some of the authentic signature data were used for training, the SOFM signature verifier. The architecture of the verifier and achieved results are discussed here and ideas for future research are also suggested.

引用

页码：272 / 275

页数：4