Fingerprint feature extraction via CNN with Von Neumann neighborhood

Yijun Lou, Fangyue Chen, Junbiao Guan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


In this paper, we study fingerprint feature extraction via CNN with Von Neumann neighborhood. The extraction was implemented by using CNN with nine input variables, and we find that the process could also be implemented with only five variables, and an easier algorithm without compromising the effectiveness. According to the CNN model with five input variables and the corresponding CNN gene bank done by Chen et al. [2006, Http1], we can determine the CNN gene easily. Simultaneously, we also find some results in one of the references are incorrect.
Original languageEnglish
Pages (from-to)4145-4151
Number of pages7
JournalInternational Journal of Bifurcation and Chaos
Issue number11
Publication statusPublished - 1 Jan 2007
Externally publishedYes


  • Cellular Neural Networks (CNN)
  • CNN gene bank
  • Fingerprint feature extraction
  • Von Neumann neighborhood

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics

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