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%.
Journal: TechConnect Briefs
Volume: 2, Nanotechnology 2008: Life Sciences, Medicine & Bio Materials – Technical Proceedings of the 2008 NSTI Nanotechnology Conference and Trade Show, Volume 2
Published: June 1, 2008
Pages: 622 - 628
Industry sectors: Medical & Biotech | Sensors, MEMS, Electronics
Topic: Chemical, Physical & Bio-Sensors