An heuristic approach for the training dataset selection in fingerprint classification tasks

Giuseppe Vitello, Filippo Sorbello, Salvatore Vitabile, Giuseppe Vitello, Vincenzo Conti

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using a lot of training examples, the proposed approach requires only 18 directional images per class. Experimental results, conducted on a consistent subset of the free downloadable PolyU database, show a classification rate of 87.59%.
Original languageEnglish
Title of host publicationSmart Innovation, Systems and Technologies
Pages217-227
Number of pages11
Publication statusPublished - 2015

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Computer Science

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