A Fingerprint Identification System Using Adaptive FPGA-Based Enhanced Probabilistic Convergent Network

被引：1

作者：

Lorrentz, P. ^{[1
]}

Howells, W. G. J. ^{[1
]}

McDonald-Maier, K. D. ^{[2
]}

机构：

[1] Univ Kent, Dept Elect, Canterbury CT2 7NT, Kent, England

[2] Univ Essex, Dept Computing & Elect Syst, Colchester CO4 3SQ, Essex, England

来源：

PROCEEDINGS OF THE 2009 NASA/ESA CONFERENCE ON ADAPTIVE HARDWARE AND SYSTEMS | 2009年

关键词：

D O I：

10.1109/AHS.2009.8

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper explores the biomeric identification and verification of human subjects via fingerprints utilising an adaptive FPGA-based weightless neural networks. The exploration espoused here is a hardware-based system motivated by the need for accurate and rapid response to identification of fingerprints which may be lacking in other alternative systems such as software based neural networks. The fingerprints are pre-processed and binarised, and the binarized fingerprints are partitioned into train- and test-set for the FPGA-based neural network. The neural network emloyed in this exploration is known as Ehnanced Convergent Network (EPCN). The results obtained are compared to other alternative systems. They demonstrate the suitability of the FPGA-based EPCN for such tasks.

引用

页码：204 / +

页数：2