Hermite/Laguerre neural networks for classification of artificial fingerprints from optical coherence tomography

Leif E. Peterson, Kirill V. Larin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Pages637-643
Number of pages7
DOIs
StatePublished - 2008
Event7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States
Duration: Dec 11 2008Dec 13 2008

Publication series

NameProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Other

Other7th International Conference on Machine Learning and Applications, ICMLA 2008
CountryUnited States
CitySan Diego, CA
Period12/11/0812/13/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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