Hermite/Laguerre Neural Networks for Classification of Artificial Fingerprints from Optical Coherence Tomography

We used forward (FNN), Hermite(HNN), and Laguerre (LNN) neural networks to classify real and artificial fingerprints based on images obtained from optical coherence tomography (OCT). Use of a self-organizing map (SOM) after Gabor edge detection of OCT images of fingerprint and material surfaces resulted in the greatest classification performance when compared with moments based on color, texture, and shape. The FNN and HNN performed similarly; however, the LNN performed the worst at a low number of hidden nodes but overtook performance of the FNN and HNN as the number of hidden nodes approached n=10.