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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 44, Iss. 5 — Feb. 10, 2005
  • pp: 647–654

Improved fingerprint identification with supervised filtering enhancement

Abdullah Bal, Aed M. El-Saba, and Mohammad S. Alam  »View Author Affiliations


Applied Optics, Vol. 44, Issue 5, pp. 647-654 (2005)
http://dx.doi.org/10.1364/AO.44.000647


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Abstract

An important step in the fingerprint identification system is the reliable extraction of distinct features from fingerprint images. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial-intelligence-based feature-extraction techniques are attractive owing to their adaptive learning properties. We present a new supervised filtering technique that is based on a dynamic neural-network approach to develop a robust fingerprint enhancement algorithm. For pattern matching, a joint transform correlation (JTC) algorithm has been incorporated that offers high processing speed for real-time applications. Because the fringe-adjusted JTC algorithm has been found to yield a significantly better correlation output compared with alternate JTCs, we used this algorithm for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.

© 2005 Optical Society of America

History
Original Manuscript: May 16, 2004
Revised Manuscript: October 14, 2004
Manuscript Accepted: October 14, 2004
Published: February 10, 2005

Citation
Abdullah Bal, Aed M. El-Saba, and Mohammad S. Alam, "Improved fingerprint identification with supervised filtering enhancement," Appl. Opt. 44, 647-654 (2005)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-44-5-647


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