Title: Image classification of artificial fingerprints using Gabor wavelet filters, self-organising maps and Hermite/Laguerre neural networks
Authors: Leif E. Peterson, Kirill V. Larin
Addresses: Center for Biostatistics, The Methodist Hospital Research Institute, 6565 Fannin Street, Suite MGJ6-031, Houston, Texas 77030, USA. ' Biomedical Optics Laboratory, Biomedical Engineering Program, University of Houston, 4800 Calhoun Road, N207 Engineering Bldg. 1, Houston, Texas 77204, USA
Abstract: Image classification was performed using Gabor wavelet filters for image feature extraction, self-organising maps (SOM) for dimensional reduction of Gabor wavelet filters, and forward (FNN), Hermite (HNN) and Laguerre (LNN) neural networks to classify real and artificial fingerprint images from optical coherence tomography (OCT). Use of a 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 colour, 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.
Keywords: Hermite neural networks; HNN; Laguerre neural networks; LNN; self-organising maps; SOM; Gabor wavelet filters; image classification; artificial fingerprints; optical coherence tomography.
International Journal of Knowledge Engineering and Soft Data Paradigms, 2009 Vol.1 No.3, pp.239 - 256
Available online: 03 Oct 2009Full-text access for editors Access for subscribers Purchase this article Comment on this article