Efficient hardware implementation strategy for local normalization of fingerprint images

Tariq M. Khan*, Donald G. Bailey, Mohammad A. U. Khan, Yinan Kong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. In essence, its task is to provide the job of foreground segmentation. A modified local normalization has been proposed along with its efficient hardware structure. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks.

Original languageEnglish
Pages (from-to)1263-1275
Number of pages13
JournalJournal of Real-Time Image Processing
Volume16
Issue number4
DOIs
Publication statusPublished - Aug 2019

Keywords

  • Embedded image processing
  • FPGA
  • Biometrics

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