ACS Applied Computer Science

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The propose algorithm finds the optimal reduced size of latent fingerprint. The algorithm accelerates the correlation methods of fingerprint registration. The Algorithm is based on decomposition and reduction of fingerprint to one dimension form by using the adoptive method of empirical modes. We choose the most appropriate internal mode to determine the minimum distance between the extremes of empirical modes. We can estimate how many times the fingerprint in the first step of the comparison can be reduced so as not to lose the accuracy of registration. This algorithm shows best results as compared to conventional fingerprint matching techniques that strongly depends on local features for registration. The algorithm was tested on latent fingerprints using FVC2002, FVC2004 and FVC2006 databases.

  • APA 6th style
Hamid, J., & Amjad, A. (2019). Optimization of fingerprint size for registration. Applied Computer Science, 15(2), 19-30. doi:10.23743/acs-2019-10
  • Chicago style
Hamid, Jan, and Ali Amjad. "Optimization of Fingerprint Size for Registration." Applied Computer Science 15, no. 2 (2019): 19-30.
  • IEEE style
J. Hamid and A. Amjad, "Optimization of fingerprint size for registration," Applied Computer Science, vol. 15, no. 2, pp. 19-30, 2019, doi: 10.23743/acs-2019-10.
  • Vancouver style
Hamid J, Amjad A. Optimization of fingerprint size for registration. Applied Computer Science. 2019;15(2):19-30.