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Proceedings Paper

New architecture for automatic fingerprint matching using neural networks as a feature finder and matcher
Author(s): Qiang Lin; Roy S. Nutter
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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)

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