Share Email Print
cover

Proceedings Paper

New architecture for automatic fingerprint matching using neural networks as a feature finder and matcher
Author(s): Qiang Lin; Roy S. Nutter
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This research explores the use of Neural Networks (NNs) to implement Automatic Fingerprint Matching (AFM) system. A new three stage architecture, consisting of a preprocessor, feature finder, and matcher stage, is shown to be successful for fingerprint matching. The NN-based AFM system used 20 fingerprints as its training set and 80 fingerprints as its test set. By dividing a fingerprint into 256 16 x 16 pixel blocks, the achieved success matching rates are 95% on the training set and 93.75% on the test set. The Feature Finder based on the Counter Propagation NN realized a high dimension reduction ratio of 256: 1. It finds a feature vector of 256 bytes from a digitized fingerprint of 512 x 512 pixels with 8-bit grayscale. This system also achieved 91.67% matching success m the cross-iferenced fingerprints. Keywords: neural networks, fingerprints, automatic fingerprint matching systems

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205165
Show Author Affiliations
Qiang Lin, West Virginia Univ. (United States)
Roy S. Nutter, West Virginia Univ. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

© SPIE. Terms of Use
Back to Top