Authors: A.A. Ilumoka, M. Talluri, M. Eng
Affilation: University of Hartford, United States
Pages: 622 - 628
Keywords: biometric systems, fingerprint recognition, neural networks, artificial intelligence
This paper describes a robust minutiae-based fingerprint identification method suitable for use in small populations. System employs two serially connected neural networks in which fingerprint feature extraction is carried out by the first network – a backpropagation neural network and matching by the second - an adaptive resonance theory network which performs the decision making task of matching acquired fingerprint to templates in a database. The approach has been applied to a real database of noisy fingerprints derived from the 2002 Fingerprint Verification Competition (FVC2002) and has achieved error rates as low as 4% at penetration rates of 100%.
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