Biologically motivated computationally intensive approaches to image pattern recognition
Future Generation Computer Systems - Special double issue: high performance computing and networking (HPCN)
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Recognition: Security and Privacy Concerns
IEEE Security and Privacy
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Hermite neural network correlation and application
IEEE Transactions on Signal Processing
Use of bias term in projection pursuit learning improves approximation and convergence properties
IEEE Transactions on Neural Networks
Gaussian filters and filter synthesis using a Hermite/Laguerre neural network
IEEE Transactions on Neural Networks
Constructive feedforward neural networks using Hermite polynomial activation functions
IEEE Transactions on Neural Networks
Classification of fingerprint images to real vs. spoof
International Journal of Biometrics
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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.