<title>New architecture for automatic fingerprint matching using neural networks as a feature finder and matcher</title>

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